Collaborative Project: 'Minorities at Risk' Database and Explaining Ethnic Violence

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The "Minorities at Risk" database produced by Ted R. Gurr has been widely cited in the scientific community. It has great potential to serve as the evidentiary arbiter of competing theories of ethnic violence. Large-scale ethnic violence is an interesting and important topic both because of the human suffering it causes and because it could be an important piece of evidence in the larger puzzle of how world politics are now evolving. Furthermore, civil and especially ethnic violence is more common now than is interstate violence of the classical sort, and it tends to be more protracted that interstate wars as well. Because of the trend of greater degrees of ethnic violence, and because of its importance for policy and for theories of world politics, the investigators of this collaborative research project foresee that the MAR database will play an increasingly important role in the search for explanations of this violence.Despite its great potential, the investigators point to some major flaws and limitations in the database. The first purpose of this project is to work with the Gurr team to improve substantially the scientific quality of the MAR data base, such that the social science community will have a much better resource for the study of ethnic violence than is currently available. The second objective of this project is to exploit the newly created database in order to make cross-sectional and time-series comparisons of violent and non-violent cases of relations between ethnic groups and states. This work is a precondition for more theoretical work on the mechanisms that move ethnic groups and states from conflict into violence.The investigators improve substantially on the extant data base by correcting errors in coding, use of vague and almost un-codable independent variables, absence of key variables leading theories of ethnic conflict, and a nearly intractable selection bias problem in the choice of cases. Empirically the project supports research assistants to improve the old variables and to add new ones. Theoretically, the investigators argue for an application of an econometric technique to address the selection bias. The final task offers preliminary results of the investigators' cross-sectional analysis of the MAR data and performs additional work to improve the database.This is a database that will have wide use in the social science community.