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This paper introduces the new Family of Ethnic Power Relations (EPR) Datasets, version 2014, which

T his paper theorizes the link between ethnicity and conflict. Conventional research relies on the ethnolinguistic fractionalization index (ELF) to explore a possible causal connection between these two phenomena. However, such approaches implicitly postulate unrealistic, individualist interaction topologies. Moreover, ELF-based studies fail to articulate explicit causal mechanisms of collective action. To overcome these difficulties, we introduce the new index N * of ethnonationalist exclusiveness that maps ethnic configurations onto political violence. This formalization is confirmed statistically in regression analysis based on data from Eurasia and North Africa.

Previous research has focused primarily on how ethnicity may trigger civil war, and its effect on conflict duration remains disputed. Rather than treating conflict as a direct consequence of ethnic cleavages, the authors argue that ethnicity per se does not affect civil war duration. Instead, its effect depends on its relationship to political institutions. They employ a dyadic approach that emphasizes the political context in which both government leaders and nonstate challengers can capitalize on the ascriptive nature of ethnicity. They show that although states can initially benefit from politicizing ethnic relations, once violent conflict breaks out, such policies may backfire on the government and make it difficult for incumbent governments to accept settlements that could terminate conflicts. Past policies of ethnic exclusion also benefit rebel organizations fighting the government, since the resulting grievances increase collective group solidarity and render individual fighters more cost tolerant. Using a new data set that codes the nexus between rebel organizations and ethnic groups, as well as information on ethnopolitical exclusion, the authors find considerable support for their propositions.

Abstract:Richardson's finding that the severity of interstate wars is power-law distributed belongs to the most striking empirical regularities in world politics. Yet, this is a regularity in search for a theory. Drawing on the principles of self-organized criticality, I propose an agent-based model of war and state-formation that exhibits power-law regularities. The computational findings suggest that the scale-free behavior depends on a process of technological change that leads to contextually-dependent, stochastic decisions to wage war. *) Earlier drafts of this paper were prepared for presentation at the University of Michigan, University of Chicago, Ohio State University, Yale University and Pennsylvania University. I am grateful to the participants of those meetings and to Robert Axelrod, Claudio Cioffi-Revilla, and the editor and the anonymous reviewers of this journal for excellent comments. Laszlo Gulyas helped me reimplement the model in Java and Repast. Nevertheless, I bear the full responsibility for any inaccuracies and omissions.

This article introduces GeoEPR, a geocoded version of the Ethnic Power Relations (EPR) dataset that charts politically relevant ethnic groups across space and time. We describe the dataset in detail, discuss its advantages and limitations, and use it in a replication of Cederman, Wimmer and Min’s (2010) study on the causes of ethno-nationalist conflict. We show that territorial conflicts are more likely to involve groups that settle far away from the capital city and close to the border, while these spatial variables have no effect for governmental conflicts.

Whether qualitative or quantitative, contemporary civil-war studies have a tendency to over-aggregate 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, this article describes a data project that geo-references ethnic groups around the world. Relying on maps and data drawn from the classical Soviet Atlas Narodov Mira (ANM), the ‘Geo-referencing of ethnic groups’ (GREG) dataset employs geographic information systems (GIS) to represent group territories as polygons. This article introduces the structure of the GREG dataset and gives an example for its application by examining the impact of group concentration on conflict. In line with previous findings, the authors show that groups with a single territorial cluster according to GREG have a significantly higher risk of conflict. This example demonstrates how the GREG dataset can be processed in the R statistical package without specific skills in GIS. The authors also provide a detailed discussion of the shortcomings of the GREG dataset, resulting from the datedness of the ANM and its unclear coding conventions. In comparing GREG to other datasets on ethnicity, the article makes an attempt to illustrate the strengths and weaknesses associated with the GREG database.