What they are fighting for: Conflict Issues in African Non- state Armed Conflicts

Transcription

1 What they are fighting for: Conflict Issues in African Non- state Armed Conflicts Nina von Uexkull & Therese Pettersson 1 Department of Peace and Conflict Research Uppsala University Nina.von_uexkull@pcr.uu.se Therese.pettersson@pcr.uu.se Paper prepared for presentation at the Meeting of the European Network of Conflict Research (EnCoRe), Amsterdam, April Abstract Why do non-state groups engage in violence against other non-state groups? Inter-group fighting without the direct involvement of the state government is a common but understudied phenomenon. In order to facilitate empirical tests of a wider range of theoretical arguments this paper introduces a data collection on conflict issues in non-state conflicts covering Africa , complementing the UCDP Non-state Conflict Dataset (Sundberg et al. 2012). We find that conflict issues cluster in three categories, territory, authority and lootable resources. Additional new information on actor characteristics, such as livelihood, external support and religious identity enables further conceptual disaggregation. Even among actors that are similar in key characteristics, such as rebel groups or communal groups, there is considerable variation in the issues the parties fight over. In a model of the sub-national determinants of non-state conflicts with different issues we provide an example of how the data can be utilised. Preliminary results indicate that temperature anomalies heighten the local risk of conflicts fought over vital resources (water, livestock or grazing land) while we do not find a significant association with other non-state conflicts. We conclude that the new data on conflict issues and actor characteristics presented will enable a more precise operationalization of the phenomenon of interest and thus allow creating theoretically delimited sub-sets of the non-state armed conflict data. 1 Johanna Ohlsson and Mihai Croicu provided excellent assistance. We also thank Johan Brosché, Hanne Fjelde, Erik Melander, Nynke Salverda and the participants of research seminars at the Department of Peace and Conflict Research, Uppsala, and the German Institute for Global and Area Studies for comments. This research was funded by the Swedish International Development Agency. 1

2 Introduction Why do non-state groups engage in violence against other non-state groups? We still have few answers to this question. Inter-group fighting without the direct involvement of the state government is a common phenomenon globally and especially in Africa. In the time period the Uppsala Conflict Data Program (UCDP) registered 419 such non-state conflicts around the world, which together caused more than fatalities. For example, in the Democratic Republic of Congo, communal conflict between the Hema and Lendu caused almost 5000 deaths between 1999 and 2003; the death toll of disputes between the Sudanese rebels SPLM/A and SSDF in the early 1990s reached similar numbers (Sundberg et al. 2012). Even though conflicts between non-state groups are usually of shorter duration and cause fewer fatalities than interstate and intrastate conflicts, this type of violence often has devastating effects on civilian populations with large-scale displacement and lost livelihood as a result (Pettersson 2010; Sundberg et al. 2012). It has also been argued that non-state conflicts may transform into other types of organised violence, underlining the need for more research on this hitherto understudied phenomenon (Brosché & Elfversson 2012). This paper builds on the argument that taking into account conflict issues in armed non-state conflict is crucial to test and develop explanations of the causes, dynamics and resolution of these conflicts. An issue can be conceived as a disputed point or question, the subject of a conflict or a controversy (Randle 1987, p.1). In the fields of civil wars and interstate disputes, the incorporation of issues in extensive studies has resulted in explanations going beyond the general determinants of insecurity. For example, studies focusing on territorial interstate disputes, separatist intrastate conflicts or religious issues have greatly advanced our understanding of these specific phenomena (e.g. Sambanis 2001; Svensson 2007; Huth 1998). Yet, an issue typology may be even more essential for conflicts between non-state groups, given the diversity of inter-group fighting. It is likely that inter-communal clashes over livestock rustling have very different dynamics than fighting over the successor to the local chief. Similarly, the determinants of inter-rebel fighting are likely to differ depending on whether groups fight over access to strategic locations or experience leadership struggles between different internal fractions. A research design aggregating all kinds of conflicts is likely to diminish or even conceal causal relationships limited to only conflicts of one kind. Although the theories tested in previous research on non-state conflict often speak more to sub-sets of conflicts delineated by issues or actor characteristics, data availability has not permitted taking into account the heterogeneity of conflicts. 2

3 In order to facilitate addressing this mismatch in the current literature, this paper introduces a data collection on issues covering Africa , complementing the UCDP Non-state Conflict Dataset (Sundberg et al. 2012). Issues cluster in three categories, territory, authority and lootable resources. Further sub-sets of these main categories are specified, such as livestock for lootable resources and grazing land for territory. Even among actors that are similar in key characteristics, such as rebel versus rebel conflict or violence between communal groups there are considerable variations in the issues the parties fight over. The use of the new data is illustrated in an application to the climate-conflict debate. Preliminary results from multinomial logistic regression of annual observations of 0.5 x 0.5 degree grid cells indicate that temperature anomalies increase the risk of conflicts over resources such as grazing land, water and livestock. In contrast, the model does not show a statistically significant association of temperature anomalies with non-state conflicts over other issues. We conclude that the data on conflict issues presented here allow us to create theoretically delimited sub-sets of armed conflict data and also can serve as a starting point for case selection for qualitative approaches. The paper is organised as follows. First, we present the rationale for taking into account conflict issues. We move on to describing how key variables are defined and measured. Then, we present patterns in the data across countries, actors and issues. Thereafter, the application of the data in a grid-based analysis is demonstrated. The final section concludes. Rationales for taking into account conflict issues The rationale for including issues builds on arguments advanced for the explanation of conflict behaviour on the interstate and intrastate level (Diehl 1992; Mansbach & Vasquez 1981). As Mansbach and Vasquez claim the shape of political contention is a function of three general factors the characteristics of the issue on the agenda, the pattern of friendship-hostility among contending actors, and the nature of the institutional context in which allocation decisions must be made. (Mansbach & Vasquez 1981, p.69). Studies on intrastate and international disputes that have incorporated conflict issues have greatly advanced our understanding of conflict causes, dynamics and resolution. There are a number of studies that focus on one issue area, such as territorial interstate disputes or separatist movements (Huth 1998; Walter 2009). We also see a direct comparison between different issue categories or actor characteristics (Buhaug 2006; Hensel et al. 2008; Sambanis 2001). For example, Buhaug (2006) showed that the relationship between regime type and conflict differs for intrastate conflicts over government and territory. 3

4 However, this conceptual disaggregation of conflict according to issues has not been systematically applied to conflict between non-state actors on a wider geographical scale to our knowledge. Nonstate conflict is defined here as the use of armed force between two organized groups neither of which is the state, which results in at least 25 battle-related deaths in one calendar year (Sundberg et al. 2012). Data on fatalities of this and related forms of sub-state violence over a large number of cases has recently become available, but none of the existing datasets specifies conflict issues (Sundberg et al. 2012; Raleigh et al. 2010; Hendrix & Salehyan 2013) 2. Two fields of inquiry have received a lot of interest, and they can serve to demonstrate the opportunities offered through our new data on conflict issues. First, there has been an interest in the composition and dynamics on the non-state side in a civil war (Cunningham et al. 2012; Fjelde & Nilsson 2012). For example, Fjelde and Nilsson (2012) suggest that a territorial control base of rebels and drug cultivation in the area increase the likelihood of inter-rebel fighting. Distinguishing according to conflict issue between inter-rebel fighting over authority and territorial control would allow us to further specify the mechanisms behind Fjelde and Nilsson s findings. Another question related to non-state conflicts has been the impact of climate change and climate variability on armed conflicts. The environmental security literature argues that climate variability negatively affects economic development and may aggravate divisions among groups, as it may increase the scarcity of vital natural resources, such as water (Homer-Dixon 1999; Kahl 2006). While this argument arguably mostly speaks to the widespread land and water resource-use conflicts, previous large-n research has included all forms of non-state conflict to test this proposition (O Loughlin et al. 2012; Raleigh & Kniveton 2012; Fjelde & von Uexkull 2012; Theisen 2012). Yet, it would be fruitful to identify violent land-use conflict, and also distinguish land-use conflicts from traditional cattle-raiding practices, which appear to be more likely during times of resource abundance due to strategic reasons (Meier et al. 2007; Butler & Gates 2012). 2 In a dataset focusing on Hindu-Muslim riots in India, Varshney and Wilkinsson include some conflict issues in the reported cause of event variable. However, this variable mixes issues and causes, and is available only for India (Varshney & Wilkinson 2006). 4

5 Given data constraints, these shortcomings are understandable, but we argue that information on the conflict issues would redress mismatches between theoretical propositions and the dependent variable studied, and would allow to empirically test a wider range of theoretical arguments. Measuring issues in non- state conflict: A new dataset In the fields of intrastate and international disputes, studies incorporating conflict issues have greatly advanced our understanding of conflict causes, dynamics and resolution. While many issues naturally differ between the interstate, intrastate and non-state sphere, we draw on widely used data collections in defining and conceptualising the data we present here. Identification of Issues The UCDP has for a long time noted the stated incompatibility between the warring parties in intrastate and interstate conflicts. The conflicts are thus classified based on what the parties themselves claim that they are fighting for. This approach has proven useful for consistent measurement of conflict over time (Gleditsch et al. 2002; Themnér & Wallensteen 2012). Ideally, we would want to use this approach for the non-state sphere, too. However, although using a great number of sources in coding, such as media reports from both international news bureaus complemented with region-specific sources like books and NGO-reports, only in 37 percent of the conflict years coded we could find statements by at least one of the warring parties. Statements from both parties were found in only 22 percent of the conflict years. 3 We thus decided to complement the coding with secondary sources, i.e. the judgements of journalists, researchers, and witnesses. This has the additional advantage that experts may provide a more correct view on conflict issues if parties have incentives to misrepresent their real aims. In order to make it possible to keep up with high reliability standards of other UCDP data of focusing on stated issues and to increase transparency, we note the type of source in the dataset (primary or secondary, referring to a specific outbreak of violence or a background issue). We also allow for multiple conflict issues. Due to lower reliability, we think that any decision on what issue is the most salient would be highly arbitrary. While this approach makes comparisons 3 This is a reason to not require conflict issues for inclusion in the conflict dataset as done for state-based intrastate or interstate conflict (Sundberg et al., 2012). 5

6 covering the whole dataset more difficult, it allows researchers to pick relevant dimensions, which we think the dataset will mostly be used for. Categories of issues Galtung (1969) describes a conflict as being the interplay between 1) the issue of contention 2) attitudes and 3) behaviour (the parties actions). The issue is thus a central element of an armed conflict (Galtung 1969, Wallensteen 2002, 34-37). However, it should be noted that rather than answering the question why parties to a conflict fight, which would be the explanation; we deal with the question over what issue they fight. An issue is here conceived as a disputed point or question, the subject of a conflict or a controversy (Randle 1987; Diehl 1992). Causes and issues should not be confounded. The aim of this project is to structure the issues in relevant categories that allow us to study sub-sets of the data that fit our theoretical expectations. Yet, established categories, such as the incompatibilities used in the UCDP state-based data, are not directly applicable. In UCDP statebased conflicts the conflict is fought either over governmental power (conflict over government) or over a territorial issue such as secession or autonomy (conflict over territory) (Gleditsch et al. 2002). Neither applicable are issues coded for interstate disputes by the ICOW project, which are regime, maritime and territorial claims (Hensel et al. 2008). In contrast to state-based violence, in non-state conflicts, we have two actors with none of them formally being in charge of the state government or part of its territory. Thus, for non-state conflicts the state government is an authority with the formal power to regulate disputes among groups on its territory. There is no such authority for a government fighting back rebellion or interstate conflicts. As the institutional context is thus very different, we would also expect non-state conflicts to somewhat differ. However, in spite of these differences, non-state conflicts do often evolve around similar issues as intrastate conflicts related to the control of territory or authority. Possibly, this is due to the fact that many conflicts, both rebel versus rebels and communal conflicts, are more likely to take place in areas or time periods of limited state-presence (Fjelde & Nilsson 2012; Raleigh 2010; Kreutz & Eck 2011). For example, as Kreutz and Eck (2011) find, communal conflict often takes place when the government is weak. Raleigh (2010) argues that communal conflicts are likely to take place in the state peripheries. In these areas of limited state control, non-state conflicts may actually resemble interstate disputes as a regulative authority is lacking. 6

7 Thus, although neither of the warring parties is the official government, we observe that many of the conflict issues fit at least one of the two dimensions, authority or territory, similar to intrastate conflicts, with a third additional dimension being lootable resources and a residual category (other). In order to further specify the issue, we created several sub-clusters of issues within the issue categories providing additional information. This laundry list of sub-clusters may for some analyses be more relevant, while we hold that it needs the authority, territory and lootable resource clusters for conceptual clarity. Further details on these issue categories follow below. Figure 1: conflict over authority Figure 2: conflict over territory Figure 3: conflict over lootable resources Authority conflicts Authority is defined here literally as the power or right to give orders, make decisions, and enforce obedience (Oxford Dictionary 2013). In the dataset authority issues are competing claims over who of the warring parties exerts control over the other group through the state apparatus or informal power structures as illustrated by figure 1. This is the case in 113 of the 368 conflict years coded (31 percent). In the most obvious form we see this in conflicts between supporters of political candidates often taking place in the context of elections. One example is the 2009 fighting in Madagascar pitting supporters of President Marc Ravalomanana against supporters of Andry Rajoelina, the mayor of the capital. Rajoelina demanded that the President should resign and tens of thousands of people took to the streets as widespread rioting erupted. After weeks of political turmoil, Rajoelina was officially declared president. Another example is the fighting over the presidency in Puntland State of Somalia in 2001, during which the acting president Abdullahi Yusuf tried to extend his mandate for another three-year period while being challenged by Yussuf Hajji Nur, a Puntland judge. We code this in the sub-cluster formal authority, which is closely related to the incompatibility over government coded for state-based conflicts (cf. Gleditsch et al. 2002). 7

8 Although formal authority is by far the most common form of conflict issue concerning authority, the authority category is more inclusive than this: In 7 of the 113 conflict years fought over authority, the issue at stake was not control of the state apparatus, but leadership within a group or a community. For example, there are several struggles over chiefdom within a community. In Nigeria in 1999, fighting erupted between the two Muslim groups Hausa and Ninzam over who should be appointed Emir, the traditional religious leader in the area. Also, disputes over leadership within rebel movements belong to this category, for example the 2002 conflict over the position as chairman of the Somali rebel group RRA (Rahanwein Resistance Army). Taking into account these differences, informal authority is coded as the second sub-cluster of the authority category. Territory conflicts Similar to intrastate conflicts, many non-state conflicts are fought over territorial issues, which concern control or use of the land for the own group, but not authority over the other warring party through the state apparatus or informal hierarchies. Figure 1 and Figure 2 illustrate how conflicts over territory conceptually differ from conflicts over authority. A full 68 percent of the conflict years coded included territory as a conflict issue. In this category we observe a great number of land-use conflicts where water or grazing lands are the bones of contentions. In 25 percent of all conflict years coded, access to grazing land or water resources, was an issue at stake. One example is when in 2011 the Kenyan pastoralist groups Borana and Turkana fought each other after the Turkana accused the Borana of uprooting them from their grazing land. However, we can also identify disputes over other territorial issues. These cases often concern the territorial borders of administrative districts, such as clashes over homelands in Darfur, Sudan, or the border of districts that are dominated by different ethnic groups in Ethiopia. These examples illustrate that conflicts over territory can be very political, although the control of territory or landuse is in question. We registered this form of land ownership issues in 186 of the 368 conflict years included in the dataset. The reason for including them in the territorial category is that the groups do not want to control the other group, but strive to control territory or its use. Again, this category is partly in line with the UCDP coding of a territorial incompatibility in state-based conflicts (Gleditsch et al. 2002). Marking the differences between land-use and other territorial conflicts, we specify grazing land/water and other territorial issues as sub-clusters of the territory category. Figure 4 illustrates the different sub-clusters share of the total number of conflicts with territorial issues. 8

9 Grazing land/ water 26% Both 10% Other territorial issues 64% Figure 4: Territory sub-clusters (percentage of a total of 252 conflict years) Conflicts over lootable resources In addition to these categories, which to some extent overlap with the incompatibilities in UCDP state-based coding, lootable resources are included as an additional contentious issue. Conceptually, we see lootable resources as a separate category as here the claims are purely economic, and not related to control over a piece of territory or the other group, but instead focus on taking the belongings of another group (see Figure 3). Thus this category is the most pure form of war as the continuation of economics by other means (Keen 2000). This type of issue is in most of the African conflicts coded so far connected to cattle rustling. 24 percent of the conflict years saw fighting over lootable resources, whereof 93 percent over livestock. Examples of conflicts over lootable resources other than livestock include fighting between the Alur and the Lendu over timber in the DRC, and rivalries over food relief between different clan militias in Mogadishu in The reasons for having lootable resources as a separate category are several: First of all, cattle-raids are very frequent in the Sahel region. For example, in Ethiopia and Kenya, they were reported as a conflict issue in 58 percent of the conflict years. There is also a whole literature on pastoralist conflicts, in particular tapping into the debate on climate and conflict (Butler & Gates 2012; Witsenburg & Adano 2009; Schilling 2012; Meier et al. 2007). Thus, there can be an interest to focus on the determinants of this category of violence. Second, as being probably the most tightly linked to economic concerns, scholars interested in struggles relating to politics (e.g. elections) may 9

10 want to exclude this form of conflict from their analysis. We specify livestock and other lootable resources as sub-clusters. Other The vast majority of the conflict issues coded fits into the three clusters of authority, territory and lootable resources. However, in some cases, we find conflicts that do not fit any of these clusters and put them into the residual category Other/unknown. Most of the time this cluster provides only information on an additional issue, while conflict issue is also coded in one of the other categories for the same conflict episode. Only in 37, or ten percent, of the 368 conflict years coded, we have conflicts where other/unknown is the only conflict issue. In nine of these 37 conflict years, we have not been able to find information on the conflict issue at stake. These have been coded as unknown. In the remaining 28 conflict years we find for example violence over religion, conflicting views of which strategies to use and retaliatory killings that escalate. For example, the Algerian opposition groups AIS (Armée Islamique du Salut) and GIA (Groupe Islamique Armée) fought each other during the second half of the 1990s. They were both against the government, wishing to replace it with an Islamic state, but fell out over strategies. GIA frequently used violence against civilians and when AIS put forward a unilateral ceasefire vowing to refrain from targeting civilians, GIA dismissed it as a sell-out of the Islamic struggle. Other examples include the conflict between the ethnic groups Banya, Foulbe and Hausa in Cameroon, during which violence erupted over whether to back a strike announced by the opposition or not and the conflict between two sub-clans in Somalia, fighting over a contract awarded to one of them to carry out repair work on a bridge. Taking a closer look at this residual category, one dimension sticks out as being both frequent (about a third of other issues) and being regarded as an important marker of conflict types on the state-based level. This is the religious dimension, which has attracted attention in large-n studies of state-based conflicts. Going beyond the territory/government dichotomy, studying the resolution of religious conflicts, Svensson (2007) shows that civil wars over religious issues are less likely to be settled by negotiated agreements than are non-religious conflicts (Svensson 2007). Most commonly religion was combined with other issues, like territory. One example is the conflict between the Somali groups Ahlu Sunna Waljamaca and Al-Shabaab, active between 2008 and 2010, which included issues of territorial control, but also religious aspects as the Sufi group Ahlu Sunna Waljamaca accused the Salafi group Al-Shabaab of destroying religious symbols and trying to ban Sufi tradition. Figure 5 shows the occurrence of different issues and sub-clusters in the data. 10

11 No of conflict years Figure 5. Issues and sub-clusters. Issues and actors As the discussion above shows, the identification of conflict issues opens up for the study of new dimensions of non-state conflict. Additional leverage may, however, be gained by also including important actor characteristics into the analysis. On a very broad level it may be fruitful to study, for example, only violence involving rebel groups or pro-government militia to capture sub-state dynamics in civil wars (Fjelde & Nilsson 2012). Similarly, communal violence between informally organised groups has been a subject of interest (Brosché & Elfversson 2012; Johansson 2010). It has been possible to make these broad distinctions since the release of the UCDP Non-state Conflict Dataset (Sundberg et al. 2012). The UCDP Non-state Conflict Dataset separates between three different types of organization among the actors. In Organization type 1, we find formally organized groups with a stated name, which are permanently organized for battle. Examples include rebel groups, militias and military factions. In Organization type 2 and type 3 we find informally organized groups that are not permanently organized for battle but who at times use their organizational structures for such purposes. Organization type 2 includes supporters of political parties and candidates while 11

12 organization type 3 includes groups that share a common identification along ethnic, clan, religious, national or tribal lines. These are commonly referred to as communal groups. However, beyond these distinctions previous research identifies several other actor characteristics that shape the dynamics and resolution of conflicts over certain issues. Ethnicity, religion, livelihood, and links to other actors mark potential lines between conflict actors and are likely to shape their relation and the issues they fight over. Ethnicity The ethnic base of groups affects their relationship to the government and to each other. For example, the dynamics and solution to sons-of-the soil conflicts over land, involving indigenous groups and alleged foreigners, are likely to be different from other conflicts over land. Moreover, links to other actors, and contagion effects are likely to be shaped by ethnicity. For example, Sambanis (2001) suggests that conflicts in neighbouring states primarily increase the risk of identity-based/ethnic wars rather than non-ethnic wars. However, as the ethnic base of actors involved in non-state conflict is coded in other ongoing projects (Fjelde & Forsberg 2013), we have not included ethnicity in the dataset for now. External support Government bias is named as a very important factor in the dynamics of inter-communal conflicts (Brosché 2012; Fjelde & von Uexkull 2012; Raleigh 2010). In rebel against rebel violence we also observe that the government may side with one of the groups. This was the case in for instance Sudan where the Sudanese government supported the SSDF (Southern Sudan Defense Force) in its non-state conflict against the SPLM/A (Sudan s People Liberation Movement/Army). Building on the definitions of the UCDP External Support Dataset we code measureable external support in the forms of weapons, military aid, training, intelligence, logistics and provision of safe havens (Högbladh et al. 2011). Admittedly, relying on these concepts we cannot capture more subtle bias, in the form of granting land rights in a biased manner, for example. This would be a very ambitious endeavor, which would, however, be plagued by more problems with arbitrary coding decisions. Focusing on relatively hard and relatively objective facts, such as weapons, we can at least capture severe government bias and support by other groups. 12

13 Livelihood Competing livelihood requirements can lead to disputes and at times to violent conflicts. Here we find distinct patterns in the relationship between settled farmers and pastoralist communities (Turner 2004; Turner et al. 2011; Benjaminsen & Ba 2009). For example, in her study of violent land-use conflicts between farmers and herders in West Africa, Eck (2011) finds that conflicting sources of legal authorities heighten the risk of land-use conflict. In that case, both the livelihood of the warring parties and the conflict issue mark important differences to other kinds of violent conflict. There is also a distinct literature on inter-pastoralist violence that often, but not always, evolves around cattle raiding (Meier et al. 2007; Butler & Gates 2012; Schilling 2012; Hagmann & Mulugeta 2008). Where the actors of the non-state conflict belong to informally organized groups ( organization type 3 ) in the UCDP non-state dataset we therefore add a variable noting the main livelihood of the group (pastoralist, farmer or agro-pastoralist, with the latter being groups that combine agriculture with livestock keeping (OWUOR et al. 2011) 4. Religion A third actor characteristic that has received attention is whether the actor identifies itself along religious lines (Basedau et al. 2011; Svensson 2007). Religious identity is coded for both formally organised groups (often rebel groups) and informally organised groups (often communal groups). The ARC/UIC (Alliance for the Re-Liberation of Somalia/Union of Islamic Courts) is a prominent example. The identification of religious actors allows us to study religious actors and religious issues separately, as has been done on the state-based level (Svensson 2007). Trends and Patterns In collecting data on conflict issues in non-state conflicts we build on the UCDP Non-state Conflict Dataset v , , which includes conflict-year information on all non-state conflicts 4 For other types of actors, e.g. organized groups or supporters of political parties, livelihood is most often not named as important. Therefore, we refrained from coding this variable for other than type 3 actors. 13

14 registered in the time period. If a non-state conflict restarts after at least one year of inactivity (i.e. not crossing the 25 fatalities threshold required for inclusion in the UCDP data) the conflict issues at stake have again been investigated. In the process of coding conflict issues we have consulted a wide range of sources, including news articles and reports from international organizations such as Human Rights Watch and Amnesty, but also books and articles written by case experts. The current, preliminary, dataset on conflict issues includes all countries in Africa except Sudan, Ghana and Mozambique and covers the time period The current version includes 61 percent of all conflicts registered in the UCDP Non-state Conflict Dataset and 84 percent of all conflicts recorded in Africa. Non-state conflicts in general are characterized by short outbursts of violence and they are seldom active for more than one consecutive year. Often the violence subsides within a month or even a few weeks or days. As a result, the number of active conflicts tends to fluctuate and it is difficult to see any trends over time (Pettersson 2010). 35 Total Territory Authority Lootable resources No of conflicts Figure 6. Conflict issues by year. 14

15 When looking at trends in conflict issues (figure 6) it becomes clear that the number of conflicts which include territorial issues seems to follow the trend in total number of conflicts with two clear peaks, one in and one in 2003 with 19 and 20 active conflicts over territory respectively. The annual number of conflicts that include authority issues was relatively stable in the 1990s with around five active conflicts per year. The years 2002 and 2003 saw an increase and in the peak year of 2003, 12 conflicts were fought over this particular issue. The number then dropped to one in 2006 and 2007, after which there have been some small fluctuations. One reason why 2003 saw the highest number of conflicts over authority was the federal and state elections in Nigeria in April and May that year, resulting in five non-state conflicts over formal authority. One example is the violence between the ethnic groups Ijaw and Itsekiri that was triggered by a dispute over electoral ward boundaries. In fact, Nigeria has experienced the highest number of conflicts over authority of all countries included in our data when looking at the entire time period Oftentimes violence has occurred in connection to elections pitting supporters of political candidates and rival ethnic groups against each other. Nigeria is home to more than 250 different ethnic groups and ethnicity has been used as a base for political inclusion. Following the end of military rule in 1999, the concept of indigeneity and its connection to the federal system was emphasized in the constitution, resulting in numerous conflicts over authority (International Crisis Group 2006). The number of conflicts over lootable resources peaked in 2000 with 16 registered cases in contrast to the average of three to four conflicts per year. Two countries in particular were responsible for this peak, Uganda and Ethiopia. Only in Uganda, eight conflicts were fought in connection to cattle rustling in Looking at the entire time period Kenya is the country with the highest number of conflicts over lootable resources. In fact, in over 80 percent of the non-state conflict years recorded for Kenya livestock raiding is a conflict issue. Livelihood and conflict issues This dataset also includes information on the non-state actors main livelihood when applicable. The categories include pastoralists, farmers and agro-pastoralists and have been coded for communal groups (organization type 3). In 75 percent of those cases we have been able to establish 15

16 the livelihood for both conflict parties. Conflicts between farmers and pastoralists have received some attention in the media and in research, as discussed above. In our data, 21 percent of the conflict years where we have coded livelihoods for both sides were fought between farmers and pastoralists and another 13 percent between farmers and agro-pastoralists. The most common conflict issue between farmers and pastoralists was territory, present in a full 88 percent of the conflict years. Authority was also a common issue, reported in 58 percent of the conflict years. Among the conflicts over territory, around 40 percent of the conflict years included the sub-cluster grazing land/water, while in almost 70 percent of the conflict years, other territorial issues were at stake in addition to or alternative to grazing land/water. Most of the farmer-pastoralist conflicts over territory took place in Kenya and in a majority of the cases one group considered itself indigenous to the area, accusing the rival group of encroaching on their traditional homeland. 28 percent of the conflict years were coded as pastoralist groups fighting each other. In 83 percent of these conflict years, territory was reported as being a disputed issue, while only 4 percent was reported as being over authority. Somewhat unexpectedly, fighting over livestock was less common than territory issues. Among the conflicts over territory, grazing land/water was the most common sub-cluster (87%) but other territorial issues were also reported in numerous cases, around 45 percent of the conflict years included this sub-cluster. Farmers fought against farmers in nine percent of the conflict years, mostly over territory. A full 94 percent of the conflict years included territory issues, while authority was disputed in 31 percent of the conflict years. No lootable resource conflicts were found between groups of farmers. For more formally organized groups, like rebel groups and militias, livelihood has not been coded. The dataset includes 99 conflict years in non-state conflicts between these types of groups. The most common conflict issue in this sub-set was control of territory, in 57 of the conflict years territory was reported as being a disputed issue. The vast majority of these conflicts took place in Somalia where, in the absence of a functioning state, different armed groups have tried to exercise control over strategically relevant towns and infrastructure. In 36 conflict years violence erupted over authority and in only two cases conflicts occurred over lootable resources. 16

17 Multiple conflict issues As discussed above, this dataset allows for multiple conflict issues for each conflict year. Around 38 percent of the conflict years included in the data have more than one conflict issue reported, while only a handful have more than two issues reported. The most common combination of issues is territory and authority, registered in 17 percent of the conflict years, followed by territory and lootable resources in almost as many cases, 16 percent. The combination of authority and lootable resources only appeared in two percent of the conflict years included in the data. The typical cases of conflicts over both territory and lootable resources are pastoralist conflicts, which include fighting over grazing land and water sources together with violent livestock raids. 78 percent of these conflicts occurred between pastoralists and/or agro-pastoralists. When it comes to the combination of territorial disputes and authority issues, the typical case is a conflict between farmers and pastoralists where the parties disagree over both in which ways land should be used but also who should rule the territory politically. One example is the fighting between Choa Arabs and Kotoko in Cameroon in 1992, triggered by cattle owned by the Choa Arabs destroying crops in disputed territory used as Kotoko farmland. At the same time, the first multiparty elections took place in Cameroon and while the Choa Arabs supported the president, the Kotoko backed the opposition. The Choa Arabs accused the Kotoko of registering aliens from distant villages to rig the elections, and violence flared. Application of the data - an illustration from the climate conflict debate As an illustration of how the new data on conflict issues can be utilised, we use it to make a contribution to the climate-conflict debate. The determinants of non-state conflict have gained major attention in the debate on the security implications of climate change and variability. The environmental security literature argues that climate variability negatively affects economic development and may aggravate divisions among groups, as it may increase the scarcity of vital natural resources, such as water (Homer-Dixon 1999). Non-state conflicts can be argued to be more likely cases of climate-related conflict compared to full-fledged civil war (Buhaug, Gleditsch, and Theisen 2008; Fjelde and von Uexkull 2012; Scheffran et al. 2012). 17

18 As also shown in the data on conflict issues we present here, non-state conflict is often fought over natural resources, such as water or grazing land, which will be affect by climate variability. For example in Ethiopia, in 2002 a severe drought made the Afar move into Kereyou territory in search of grazing land and water leading to clashes between the groups (Reliefweb 2002). Although an important causal mechanism between climate and inter-group conflict goes via increased scarcity of vital resources as this example illustrates, large-n studies on a wider geographical scale could only study general patterns of inter-group violence (Fjelde and von Uexkull 2012; Raleigh and Kniveton 2012; Theisen 2012). This new data on conflict issues permits us to take a closer look at the hypothesised relationship between climate variability and conflict across a large number of cases. In particular, it can be expected that violence over renewable resources, such as water, grazing land and livestock coded in this dataset, is a more likely effect of climatic anomalies than other kinds of conflicts. Research design As an illustration of how the new data can be used, this expectation is modelled in a multinomial logit model on the sub-national level. We use annual observations of 0.5x0.5 degree grid cells based on the PRIO-GRID structure as our unit of analysis (Tollefsen, Strand, and Buhaug 2012). Our preliminary analysis covers the years and all countries where data on conflict issues are available (i.e. Africa except for Sudan, Ghana and Mozambique in this version). Data on the subnational level is suitable to study spatially limited phenomena such as environmental changes and armed conflict (Theisen, Holtermann, and Buhaug 2011). The dependent variable in the following models shows three different outcomes, no non-state conflict events in cell (0), non-state conflicts over natural resources (1) and non-state conflicts over other issues (2). The non-state conflicts over natural resources are coded 1 for all conflicts with the issues livestock and grazing land/water found in our data; conflicts over other issues are the remaining non-state conflicts (2). Data for the location of conflict events is obtained by merging the issues data with the UCDP geo-referenced event data (UCDP-GED v 1.5) (Melander and Sundberg forthcoming 2013). If a non-state conflict crosses the 25 battle-related deaths threshold during one year we use the location of the events resulting in those deaths to code our dependent variable. Of all 789 cell-year observations with non-state conflict incidences coded, about 25% percent are coded as being conflicts over renewable resources (livestock and grazing land/water). For the independent variables we rely on the data included in the PRIO-GRID data collection, 18

19 which have been used in studies contributing to the climate-conflict debate (Tollefsen, Strand, and Buhaug 2012). One key variable used to model climate variability is deviations in temperature, which several studies claim to be most robustly related to conflict (Burke et al. 2009; O Loughlin, A. Linke, and Witmer 2013; O Loughlin et al. 2012; Hsiang and Burke 2013). Temperature anomalies are calculated as standardised standard deviations from the long-term mean ( ) based on data from the University of Delaware. We use this variable as the main indicator in this application. The following control variables are used to account for alternative explanations: We include per capita Gross-Cell Product from Nordhaus (2006) to measure economic development and a variable measuring Population in the grid cell (CIESIN and CIAT 2005). We also include a temporal spatial lag of civil war events (measured for the same or neighbouring grid cells at t-1 based on UCDP-GED events). Proximity to civil wars has been found to be positively associated with non-state conflict risk (Fjelde and Nilsson 2012; Fjelde and von Uexkull 2012). In order to take into account spatial and temporal dependence in the non-state conflicts, we also include a temporal spatial lag for non-state conflict events in the same or neighbouring grid-cells at t-1. Not only the spatial lags, but also all other independent variables are lagged by one year. Results Table 1: Results Multinomial Logistic Regression, Resource and Non-resource conflicts Model 2 Resource conflict Non-resource conflict p (β res =β2 non-res) Temperature anomaly t ** (0.0821) GCP pc ln t *** (0.104) Population ln t ** (0.0518) Spatial lag NS t *** (0.217) Spatial lag CW t *** (0.242) Constant *** (0.968) (0.0451) *** (0.0864) *** (0.0513) *** (0.161) *** (0.168) *** (0.785) Observations Standard errors, in parentheses, have been adjusted for clustering by grid-cells. The far right column gives the probability that the coefficients for resource and non-resource conflicts are identical. * p < 0.10, ** p < 0.05, *** p <

20 In a multinomial logit model, the coefficients give information in relation to the reference category (no conflict in this case). The coefficient for deviations in temperature is positive and significant for resource conflicts, while it is not significant for other non-state conflicts. Using Clarify, a move from the 10th to the 90th percentile on the temperature variable is associated with an increase in the predicted probability of resource conflicts by 87% (from to , the change is statistically significant). 5 The results from the multinomial logit model thus indicate that temperature anomalies indeed are significantly associated with an increased risk of non-state conflict. However, they seem to most likely lead to scrambles over natural resources such as livestock, grazing land and water, rather than to conflicts over other issues. Figure 7. Predicted probability of resource conflict for temperature deviations (t-1)based on simulations using Clarify (King et al. 2003). For the control variables interesting differences are observable. Notably, economic development (GCP pc) is negatively associated with resource conflicts, whereas it is positively associated with conflicts over other issues. The result confirms the findings of earlier research on communal violence, portraying this type of conflict as tending to take place in marginalised areas in state peripheries (Fjelde and von Uexkull 2012; Raleigh 2010). The result for conflicts over other issues may reflect that among these conflicts there are strategic scrambles over valuable territory or 5 Note that the result is similar when dropping 95% of cells with a zero in the dependent variable, which could be motivated in order to reduce dependence in the data (cf. Buhaug et al. 2011). A more 20

21 valuable lootable resources, something that is reflected in higher economic development (Fjelde and Nilsson 2012) or violence taking place in cities. For other variables there are some differences in the magnitude of the coefficients, but the direction of the association is the same and as expected. The results indicate that more populous areas run a higher risk of conflict as well as regions with previous civil war or non-state conflicts in the same or neighbouring regions. The findings of this preliminary assessment thus show that analytical leverage can be gained by taking into account that the conflict issue may mark different types of conflict that differ in their determinants. Extensions of this initial study may focus on the role of different actor characteristics, further disaggregate conflict types into conflicts over livestock and water, as well as extend the analysis to other indicators of climate variability and test the robustness of the result to changes in the statistical model. Discussion and conclusion The new dataset includes information on conflict issues in non-state conflicts. In addition, the dataset gives information on actor characteristics like the main livelihood, support from other actors, and religious identity. We argue that previous research on non-state conflicts has suffered from the lack of data on conflict issues, an aspect that for a long time has been included in studies on interstate and intrastate armed conflict and that has significantly enhanced our understanding of causes, dynamics and resolution of such conflicts. The new data collection effort presented in this paper shows that issues in non-state conflicts tend to cluster in three categories, territory, authority and lootable resources. Even among groups that are similar in key actor characteristics, such as rebel versus rebel conflict or violence between communal groups, there is considerable variation in the issues the parties fight over. A first look at the new data revealed that in 38 percent of the 368 conflict years coded more than one issue was at stake. For example, many conflicts over territory are fought between pastoralist groups who also clash over cattle-rustling. The data also shows that the combination of territorial issues with conflicts over authority is by no means rare in non-state conflicts. The preliminary results we present for subnational variations in conflicts fought over resources affected by climate variability 21

22 and conflicts over other issues indicate that there is a higher risk of resource conflicts following anomalies in temperature. In contrast, the association between temperature anomalies and other conflicts is not statistically significant. Naturally, studying sub-sets of conflicts comes with a cost and is not always desirable. First of all, the valid and reliable measurement of conflict issues is inherently difficult. When dealing with nonstate conflict as we do here, the stated objectives of the warring parties are most often not readily available. The reliability of the data therefore inevitably varies. Furthermore, when the theoretical argument is rather relating to the occurrence of organised violence in general, studying sub-sets of the conflicts would just limit the generalizability of findings and may unnecessarily reduce statistical power by disaggregating further an already rare event. These trade-offs will need to be weighted according to the specific research question and focus. However, given the plausible variation in causes and dynamics of conflicts we can infer from case studies on non-state violence, it seems useful for many research questions to test theoretical propositions on sub-sets of armed conflict data. We hope that this new data we present here will offer the opportunity to study more refined explanations of distinct sub-sets of non-state conflict over a large number of cases. References Adano, W.R. et al., Climate change, violent conflict and local institutions in Kenya s drylands. Journal of Peace Research, 49(1), pp Basedau, M. et al., Do Religious Factors Impact Armed Conflict? Empirical Evidence From Sub-Saharan Africa. Terrorism and Political Violence, 23(5), pp Benjaminsen, T.A. et al., Does climate change drive land-use conflicts in the Sahel? Journal of Peace Research, 49(1), pp BENJAMINSEN, T.A. & BA, B., Farmer herder conflicts, pastoral marginalisation and corruption: a case study from the inland Niger delta of Mali. Geographical Journal, 175(1), pp Brosché, J., Conflicts over the Commons Communal Conflicts in Darfur and Eastern Sudan. Presented at Workshop in Political Theory and Policy Analysis. Bloomington, University of Indiana. Brosché, J. & Elfversson, E., Communal conflict, civil war, and the state: Complexities, connections, and the case of Sudan. African Journal on Conflict Resolution, 14(1), pp Buhaug, H., Relative Capability and Rebel Objective in Civil War. Journal of Peace Research, 43(6), pp Buhaug, H., Gleditsch, N.P. & Theisen, O.M., Implications of Climate Change for Armed Conflict. In Social Dimensions of Climate Change. World Bank. Buhaug, Halvard et al It s the Local Economy, Stupid! Geographic Wealth Dispersion and Conflict Outbreak Location. Journal of Conflict Resolution. Burke, M.B. et al., Warming increases the risk of civil war in Africa. Proceedings of the 22