Geo-Referencing of Ethnic Groups: Creating a New Dataset*

Transcription

1 Geo-Referencing of Ethnic Groups: Creating a New Dataset* Lars-Erik Cederman 1, Jan Ketil Rød and Nils B. Weidmann 1 1 International Conflict Research ETH Zurich 809 Zurich, Switzerland (cederman, weidmann)@icr.gess.ethz.ch Department of Geography Norwegian University of Science and Technology (NTNU) and Centre for the Study of Civil War (CSCW) International Peace Research Institute, Oslo (PRIO) 791 Trondheim, Norway jan.rod@svt.ntnu.no February 3, 007 Abstract. Whether qualitative or quantitative, contemporary civil-war studies have a tendency to overaggregate empirical evidence. In order to open the black box of the state, it is necessary to pinpoint the location of key conflict parties. As a contribution to this task, the current paper describes a data project that serves to geo-reference ethnic groups around the world. Relying on maps and data drawn from the classical Soviet Atlas Narodov Mira, we use geographic information systems (GIS) to represent the groups as polygons. We explain our methodological choices and procedures before outlining a series of applied research projects, which will make use of the generated data. *) Prepared for presentation at the ISA Annual Convention in Chicago, February 8- March 3, 007. We are grateful to the staff of the map collection at the Zurich Central Library, especially Hans-Peter Hoehener, and to Helena Kusch, Doreen Kuse and Olga Nikolayeva for their excellent research assistance. Nils Weidmann is supported by ETH (Research Grant TH -/0-3). 1

2 1. Introduction The role of ethnicity in conflict processes remains as controversial as ever. The ethnic wars following the end of the Cold War triggered a surge of interest in ethnic conflict (Posen 93; Kaplan 93; for references see Cederman 00). In this wave of scholarship, International Relations specialists attempted to apply their traditional tools, that had been developed to study primarily superpower relations and other interstate exchanges. In contrast, more recent research has turned the attention more specifically to civil wars. Inspired by a prominent project funded by the World Bank, a number of political economists have expressed serious doubts as to whether ethnic grievances really drive such conflicts (Sambanis 00; Fearon and Laitin 003; Collier and Hoeffler 00). Yet, because the current literature on civil wars relies extensively on cross-national statistics, the question of what mechanisms drive observed macro patterns can only be answered indirectly, and sometimes not at all (Sambanis 00). This problem pertains to the effect of prominent indicators, including GDP per capita, and to trace dependence (e.g. special issue). Indeed, ethnicity belongs to the concepts that remain under-theorized and over-aggregated. In particular, the use of the ethno-nationalist fractionalization index has so far not been supported by convincing causal mechanisms (Cederman and Girardin 007). Without disaggregating the country cases, then, it is difficult, or even impossible, to tell one candidate explanation from another. It is clear that the black box of the state has to be opened in order to specify causal micro-level mechanisms that produce observed aggregate statistics. In this working paper, we present a data project that attempts to attain this goal by disaggregating ethnicity spatially. More specifically, our goal is to place ethnic groups on the map as a way to locate key actors of conflict processes. Conflict data have already been geo-coded (Buhaug 003), but instead of relying on the circular representation of conflict zones we will used improved, more accurate, polygon representations for the areas affected by conflict. We intend to use these spatial representations to infer whether ethnic groups are actually involved in fighting as hypothesized.

3 In the following, we proceed in five steps. First, we survey possible data sources that could potentially support spatial disaggregation of ethnicity. Second, we briefly discuss the application of GIS to conflict research. The following section describes the project s structure and methods, before turning to a section on project management. The paper ends with a section on future applications and research questions relevant to our data project.. Data sources for spatial disaggregation of ethnicity In the literature, ethnicity has typically entered analysis of conflict processes either as qualitative, historical entities or as quantitative indices, such as the ethno-nationalist fractionalization index (ELF) (Fearon 003; Posner 00) 1. In neither case, however, space has played a prominent role in the development of causal arguments. Historical and other qualitative accounts of ethnic conflict occasionally provide maps that show the spatial distribution of groups, but such information is hardly ever supplied for a larger sample of states (e.g. Horowitz 8; Herbst 000). While the quantitative literature does offer some references to settlement concentration, such information is usually narrowed down to a one-dimensional variable (e.g. the Minorities at Risk Project, see Gurr 93). However, to our knowledge, there are no data sources that systematically pin down the location of ethnic groups in a large number of comparable cases. Thus, the exact location of the key actors in ethnic conflicts is still obscure. This raises the question from where such information could be drawn. There are several candidates: Linguists have developed detailed maps of language diffusion, see for example Ethnologue (Grimes 000). However, such data resources are problematic because politically relevant ethnicity depends on more than language, for example religion and other cultural traits (Barth 9). Moreover, linguistic charts are typically either too detailed to serve as a guide to ethnic group delimitation or too sketchy as they often 1 Alternatives to the fractionalization index exist, such as measures of polarization (Reynal-Querol 00; Montalvo and Reynal-Querol 00) and ethnic domination (Elbadawi and Sambanis 00; Collier and Hoeffler 00). 3

4 represent a linguistic group with a point, thus making delimitation or inference on spatial dissemination fuzzy. The same can be said for religious maps, see e.g. Another possibility would be to infer the location of ethnic groups from census or survey data (Laitin 98). Yet, such an approach is only viable where such data contains references to ethnicity, which is often not the case. Furthermore, it also hinges on the presence of a reasonably fine-grained provincial structure. Where federal subunits are large, the needed degree of spatial disaggregation may never be attained. For the aforementioned reasons, we have chosen to rely on data and maps from the well-known Atlas Narodov Mira (Bruk ), which stems from a major project of charting ethnic groups undertaken by Soviet ethnographers in the 0s. Their efforts bore fruit in the extensive but still un-translated, which covers the entire world. It is widely used in contemporary research, especially when it comes to calculating the ELF index (e.g. Taylor and Hudson 7). Other, more recent lists of ethnic groups exist, but none of them comes with maps (cf. Fearon 003). The Atlas has several strengths: it is complete and carefully researched, it relies on a uniform group list that is valid across state borders, and it provides high-quality maps. Among the weaknesses, it should be stressed that the Atlas is based on the situation in the 0s and thus clearly outdated compared to the current situation. However, in most cases, ethnic settlement patterns exhibit inertia, so it seems reasonable to depart from this dataset, at least initially. Any extension or updating would be an extremely labor intensive undertaking, for which we presently do not have any resources. All the same, such a project would obviously be very desirable and should be seen as a longterm goal. Before describing our use of the ANM, a few words about previous use of GIS in conflicts studies are in order.

5 3. GIS-based conflict research Most definitions of geographical information systems (GIS) include components of capture, storage, manipulation and presentation of geographical data (e.g. Martin 9). GIS stores geographical data either as discrete objects or as continuous fields, two approaches often referred to as the vector and raster data model, respectively. The vector data model represents phenomena on the earth as points, lines, or polygons. In conflict applications, points typically represent cities, diamond sites (Gilmore et al. 00) or conflict centre points (Gleditsch et al. 00). Lines may represent linear features like road network, pipelines or international boundaries (Starr and Thomas 00). Finally, polygons can be used to delineate countries, geographic domains of conflict zones (Buhaug and Gates 00) or petroleum fields (Lujala et al. 00). In a raster representation, geographic space is divided into a rectangular array of cells, each of which is usually square. Geographic variation such as elevations or temperatures is expressed by assigning properties or attributes to these cells. Any other continuous phenomenon for which one may measure a z-value for every possible position is suitable to represent as raster data (Longley et al. 001). Density measures such as population densities (Dobson et al. 003) and density of forested or mountainous terrain are also often conceptualized as continuous data and represented as raster data. For disciplines in which geography or location matters GIS have the potential to serve as a useful tool. There are many examples of studies that draw a link between geography and war while paying special attention to geographical features like population density, natural resource richness, and ethnic fractionalization. However, as Buhaug and Lujala (00) point out, empirical studies have so far been less than successful in establishing a clear link between the geographical distribution of physical and human factors and civil war. Buhaug and Rød (00) argue that a considerable portion of the empirical study of civil war suffers from a disturbing mismatch between theory and analysis. Rough terrain, for instance, is ideal for guerilla warfare since it is difficult for government forces to control. Furthermore,

6 closed forest provides cover against detection. To investigate the link between rough terrain and conflict, both Fearon and Laitin (003) and Collier and Hoeffler (00) use the average share of mountainous terrain (the latter also added forested terrain) in the country as predictors of onset of conflict, thus neglecting the location of these factors. For a study on African civil war between 70 and 001, Buhaug and Rød (00) found no support for the rough terrain proposition using local measures. Through the use of GIS, a joint effort from PRIO and NTNU has resulted in subnational dataset on diamonds (Gilmore et al. 00) and petroleum fields (Lujala et al. 00). Work is also underway to improve circular conflict zone polygon generated by Buhaug and Gates (00). As they note, a negative consequence of the assumed circular shape of the conflict zones is that the measured scope inevitably covers some areas not affected by conflict, thus overestimating the total area of the civil war (Buhaug and Gates 00:). In principle, there is no reason why ethnicity could not be geo-coded in precisely the same way as other conflict determinants. The problem with using country measures is that mountains, closed forest and, indeed, ethnic settlement patterns have a tendency to vary across space, which means that aggregated measures are poor indicators of local conditions. Following the lead of Buhaug and Rød (00), we complement the habitual country-level of analysis with an explicitly spatial, disaggregated approach.. Project structure This section introduces the structure of the Atlas Narodov Mira (ANM) and the data contained therein before describing how we use this data for the GREG project. The ANM consists of 7 ethnographic maps, covering all regions of the world at various scales. Each map shows the geographic distribution of the relevant groups, indicated by colored areas. In addition to the color coding, the areas are marked with numbers which refer to the respective group's number in the legend. Most areas are coded as pertaining to one group only, but in some cases there can be up to three groups The ANM includes maps on population density as well which we do not include in our coding.

7 sharing a certain territory (although the latter case is quite rare 3 ). This is indicated on the map by a striped fill of the respective areas. Figure 1 shows a part of the map covering the Former Yugoslavia. Note that for each area there is one or two numbers indicating the respective group. Figure 1 about here The ANM also provides information about minor scattered groups in an area. The presence of these groups is indicated by symbols rather than areas. To give an example, in the above map minor occurrences of group (Croats, square symbol) can also be found in Northeast Slovenia. Sparsely populated areas can be distinguished from others by their grey raster fill and the missing group color fill. However, for these regions group presence is still indicated by symbols and numbers as explained above. Unpopulated regions are left white. Apart from the map collection, the ANM features a statistical appendix complementing the geographic information. The appendix contains two major lists. The first one gives the full set of groups mentioned in the ANM along with their relative population sizes within each country. The second list reverts this information and lists all countries together with their groups. It is the latter list that has served as a basis for the computation of ELF scores in the literature (Taylor and Hudson 7). Since the GREG project aims at creating a dataset of geographic locations of ethnic groups, we focus on the information contained in the ethnic maps of the ANM. Our initial goal is to create a GIS dataset providing a global map of ethnic group locations based on the maps included in the Atlas. In order to be able to work as closely as possible on the ANM data representation, we chose to use a GIS vector data format. More precisely, group locations in our dataset are represented by polygon features which correspond to the areas indicated in the ANM. The polygons are created to be non-overlapping, i.e. such that each geographic location is covered by exactly one polygon (leaving out the unpopulated regions for which there is no group information 3 For example, on the African continent there are 100 polygons. Only one of them, situated in South Africa, has three overlapping groups (17 polygons represent two overlapping groups). 7

8 available). Like in the Atlas, polygons can represent up to three groups. Group names and IDs are stored in the polygon's attribute table. Our coding does not include the population density information given by the ANM. As mentioned above, sparsely populated areas are indicated as such in the maps. Following the GIS approach of separating data of different types into different layers, we decided not to encode the ANM's population density, but to focus rather on ethnic group location only. However, for most applications one will need population data in addition to the ethnic coverage. This can be achieved by adding a population layer from databases such as Gridded Population of the World (Socioeconomic Data and Applications Center (SEDAC) 00) or LandScan (Oak Ridge National Laboratory 003) to our ethnographic layer. To give an example, Figure shows a map of Nigeria, with polygons representing ethnic groups. These polygons are superimposed on a grid with population data from (interpolated from a UNEP dataset with 0 and 70 population numbers). Figure 3 and about here Figure 3 and Figure show two readily coded ethnic maps of our dataset: the states of the former Yugoslavia, and Hungary. In these maps, groups are indicated by numbers and correspond to group names as follows: 1 Czechs 13 Germans Slovaks 1 Austrians 3 Slovenes 1 Aromani (Vlachs) Croats 17 Italians Bosnians 18 Albanians Serbs Hungarians 7 Montenegrins 0 Turks 8 Macedonians 9 Bulgarians 8

9 . Project management We had to find a way to split geo-coding and translation since it was unlikely to find enough people fluent in Russian which could also do the GIS encoding. Fortunately, the ANM uses not only color fill to indicate groups but also numbers. So page and group number together are sufficient to identify a single group. Therefore, we were able to split the entire coding effort into a geo-coding and a translation task which are carried out independently. In this section, we describe both tasks and the way we manage our collaboration. Lars-Erik Cederman and Nils Weidmann at ETH are providing the computational infrastructure for the project. This includes an online repository where data can be exchanged as well as a collaboration tool especially created for this purpose (see below). In a first step, we created scans of the ethnic maps in the ANM which are now made available to all coders on the GREG web server. The geo-coding task is carried out by Jan Ketil Rød and his students at NTNU, using ArcGIS 9.0. The basic unit for the coding is a country. We started with GIS country shapes of to fit the Atlas data as accurately as possible. The geo-coding first requires the geo-referencing of the scanned maps, i.e. the linking of map locations to geographical coordinates which the GIS is able to handle. Once a map has been successfully referenced for the country being coded, group polygons are created according to the underlying map following a technique called screen digitizing or head-up digitizing (Longley et al. 001). In doing so, the coder has to make sure that the polygons completely cover the country's area, and that there are no overlapping polygons or gaps between polygons. For each polygon, the information about the group it represents is then entered into the dataset's attribute table, using the page number of the map and the group number as given in the legend. Note that because polygons can represent up to three groups, we require three group number columns. The geo-coding task is supported by an online collaboration tool, enabling the coders to work independently of each other. This tool is essentially a database-backed dynamic website listing the countries to be coded, the coder the country is assigned to, and the status of the coding. Coders can enter this website and assign a country to 9

10 themselves or update the coding status. This way, we can quickly summarize the status of the project and avoid multiple coders erroneously coding the same country. At the time of writing, 1 out of 3 countries have been finished. The translation task is carried out by a Russian student at ETH and coordinated by Nils Weidmann. In the first step, the group list used in the ANM is currently being put together. In the second step, the map legends will be translated. More precisely, we will create a mapping from page and group numbers to group names. This way, we will later be able to join the geographic dataset (which so far uses page and group numbers only) and the group list in order to create the final dataset. Once the dataset has been put together, we will validate the data taken from the Atlas using ELF indices. More specifically, we will compute ELF values from the Atlas data which we can then compare to ELF indices from other sources. ELF indices can be computed from ANM data by adding a population layer and calculating group sizes as the total population covered by the group's polygon(s). Using GIS population data from 90 (Socioeconomic Data and Applications Center (SEDAC) 00) and ELF indices from 8 (Roeder 001) one could almost neglect temporal changes. However, the group size estimate is rather crude because it assumes everyone settling in a region covered by a group polygon to be member of that group. Yet, this procedure will provide a first assessment of the accuracy of the ANM data.. Applications The great promise of the current project is that it will allow researchers to pinpoint the location and shape of ethnic settlements and thus get a much better grasp of the nature of ethnic groups as collective actors in conflict processes. To be sure, much of the literature is marred by excessively individualist conceptualizations of ethnicity that bear no resemblance with real-world processes (Cederman forthcoming). There is a crucial lack of plausible accounts that link macro patterns with micro-level agency. Note that we are using ESRI's GIS country scheme which lists some areas (especially islands) as separate countries, although they are in fact part of another country. However, the precise definition of a country is not relevant for the coding itself, as we only need to have small subunits which can be treated independently. 10

11 The new dataset could serve as an antidote to such reductionist conceptualizations by indicating the territorial boundaries of the groups in question. Whereas there is virtually no limit to the number of actor constellations that could be investigated with this research tool, in this section we outline a small number of ongoing projects that all rely on a center-periphery approach to inter-ethnic relations in multi-ethnic states. Project 1. Articulating the Geo-Cultural Logic of Nationalist Insurgency Lars-Erik Cederman This project explores the mechanisms driving existing research on civil wars by relying on agent-based modeling. Here the focus is on the key findings of Fearon and Laitin (003), especially their suggestion that state strength and rough terrain are key determinants of civil wars. Their theory of insurgency suggests that weak states cannot control their peripheral territories, which are consequently more prone to uprisings. However, according to Fearon and Laitin (003), the ethnic composition is irrelevant to their logistical explanation. In contrast, Cederman (forthcoming) argues that the findings pointing to weak states and territory as key factors are also compatible with non-materialist explanations highlighting the role of ethnic identities. Drawing on Rokkan (99), whose conception of center-periphery relations hinge on cultural distinctiveness as well as geographic and logistical considerations, this research sets up an artificial, computational world in which controlled experiments can be performed. In such a setting, it is possible to articulate geo-cultural mechanisms that generate macro-level patterns that are similar to those found by Fearon and Laitin. In contrast to their finding, however, the agent-based model suggests that conflicts can result from imperfect cultural state penetration, incomplete national mobilization and nationalist collective action. In the absence of empirical geo-coding of ethnicity, the computational findings merely show that an alternative, constructivist explanation is theoretically plausible. Whereas such a result is interesting in its own right, it fails to resolve the debate. In 11

12 order to make progress in this respect, there is no substitute for real-world evidence, which is the rationale of the current data project. Based on the geographic information resulting from this research, it will be possible to at least partly isolate ethnic from materialist mechanisms. Project. Disaggregating Ethnic Conflict: A Dyadic Model of Exclusion Theory Halvard Buhaug, Lars-Erik Cederman and Jan Ketil Rød In this empirical project (Buhaug et al. 00), we focus on relation-specific causes of ethnic conflicts. The goal is to disaggregate both ethnicity and conflict to the level of explicitly geo-coded center-periphery dyads. This is an important goal, because, so far, conflict data has typically been recorded at the country level. Whereas this is a satisfactory simplification in small countries with few conflict groups, the results can be seriously misleading in the case of large countries, such as Russia. The political, cultural and geographic conditions pertaining to Chechnya do not necessarily apply elsewhere in the country. Likewise, the remote Indonesian province of Aceh is clearly not representative of Indonesia as a whole. Focusing specifically on ethno-nationalist conflicts pitting peripheral ethnic groups against central governments, this analysis allows us to measure the center-periphery power balance as terrain-adjusted demographic proxies. This is an important contribution to the literature, because unlike interstate warfare, civil wars do not lend themselves to straight-forward measurement of power balances. In addition to establishing a link between the dyads and conflict data, it is essential to pinpoint the location of the ethnic groups themselves. If the location of the conflict parties were known, it would be possible to discount power resources, such as crude group sizes, that have so far been used a approximations of group power (Cederman and Girardin 007) For sure, other indicators of power, such as logistical factors and other communication resources could be included in the analysis. To this end, we rely on GIS-coded layers of empirical information, including (1) newly geo-coded dyadic conflict data, () data on ethnic groups access to power, (3) 1

13 newly geo-coded data on the location of ethnic groups, and () geo-coded logistical data on distances and terrain. By disaggregating the conventional country-level analysis to the level of centerperiphery dyads, we are in a position to formulate and validate hypotheses that feature explicit, group-level mechanisms and motivations. Such an analysis moves the literature from abstract correlations based on merely postulated micro foundations to the articulation of theoretical models featuring explicit actors and actions. Ultimately, in this way, we hope to address the important theoretical questions about whether and how ethnicity plays a role in internal conflict processes. Project 3. Critical Configurations: Settlement Patterns and Political Violence Nils B. Weidmann During the last century, ethnic conflicts have claimed the lives of millions. Various scholars have emphasized geographic settlement patterns of ethnic groups in a country as co-determinants of these conflicts (see for example Toft 003). However, empirical work on the relation between ethnic settlement patterns and violence is sparse. Are certain ethnic settlement patterns more conflict-prone, and if so, what are those critical configurations and why do they express higher levels of violence? Few studies have systematically addressed these questions and the issue remains controversial. Weidmann s dissertation project aims to fill this gap. It starts by creating a global dataset of ethnic group locations which will provide the basis for our research. This data collection effort is closely linked to the GREG project described in this paper. Using this data, Weidmann plans to address three research questions. First, the project tests for the general impact of settlement patterns on civil war. Second, it will examine the location of conflict events in relation to settlement patterns: Which areas in the settlement configuration of a state are more likely to be affected by violence? Third, the research plan calls for examination of the endogeneity of settlement patterns: Given that settlement patterns influence civil war, how do civil wars in turn change the ethnic map of a state? 13

14 The research proposed here has three objectives. First, by employing GIS to collect data on ethnic groups, it will provide a more appropriate, systematic way of dealing with spatial data on ethnic groups than what has been done before. Second, using this data, it will be able to test for the impact of settlement patterns and resolve the theoretical predictions that so far remain controversial (Weidmann 00). Third, the project will go beyond establishing empirical facts by also providing an analysis of the underlying micro-level mechanisms in two case studies (the Balkans and the Great Lakes Region in Africa). Theoretical assumptions derived from these case studies will be used to implement an agent-based computer model such that the model output can be validated against empirical evidence. 1

15 Figure 1: Former Yugoslavia map from the ANM. 1

16 Figure : Ethnic map of Nigeria, superimposed on a poplation grid. 1

17 17!!!! ,, 9,1, 9,, 7,1, 0,, 1 0, 1, 18,7,17,, 7,,, 1,,, 1,, 0, Tirana Tirana Ljubljana Ljubljana Zagreb Zagreb Skopje Skopje Sarajevo Sarajevo,17 17 Sofiya Belgrade Naples Naples Thessaloniki Bari Timisoara Albania Albania ovenia ovenia Macedonia Macedonia Italy Italy Hungary Hungary Romania Romania Croatia Croatia Serbia & Montenegro Serbia & Montenegro Greece Greece Bosnia & Herzegovina Bosnia & Herzegovina Bulgaria Bulgaria CS CS HU HU RO RO BA BA HR HR GR GR BG BG Budapest Sl Sl Austria Austria Slovakia Slovakia Germany Germany IT IT AT AT Figure 3: Ethnic map of the former Yugoslavia.

18 ! CZ AT 3 Slovenia HR Zagreb Czech Republic Vienna Austria 13, HU Hungary Croatia Danube Danube Bratislava 13, 13, , 13 13, 13, 13, 13 Budapest CS Serbia & Montenegro,, Timisoara 1, SK Slovakia UA 1 RO Romania Ukraine Figure : Ethnic map of Hungary. 18

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