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Interdisciplinary Laboratory for Computational Social Science (iLCSS)

 


Amar



The All Minorities at Risk (AMAR) project is a university-based research project that monitors and analyzes the status and conflicts of politically-active communal groups in countries with a current population of at least 500,000. The project is designed to provide information in a standardized format that aids comparative research and contributes to the understanding of conflicts involving relevant groups.



The project was founded in 1986 as the Minorities at Risk (MAR) data by Ted Robert Gurr, one of the preeminent scholars of political violence and ethnic conflict. Since 1988, the Center for International Development and Conflict Management (CIDCM) at the University of Maryland has hosted the project. The MAR Project has been led by Ted Robert Gurr (1986-2004), Christian Davenport (2004-2006), and Jonathan Wilkenfeld (2007-2010). The AMAR project is led by Jóhanna Birnir (2010-present). The MAR and AMAR project have been funded by various agencies, including the Carnegie Corporation, the Hewlett Foundation, the National Consortium for the Study of Terrorism and Responses to Terrorism (START), the National Science Foundation, the State Failure (now Political Instability) Task Force, the United States Institute of Peace, the U.S. Department of Homeland Security, and the Department of Government and Politics at the University of Maryland.


Publications


The article introduces the All Minorities at Risk (AMAR) data, a sample of socially recognized and salient ethnic groups. Fully coded for the forty core Minorities at Risk variables, this AMAR sample provides researchers with data for empirical analysis free from the selection issues known in the study of ethnic politics to date. We describe the distinct selection issues motivating the coding of the data with an emphasis on underexplored selection issues arising with truncation of ethnic group data, especially when moving between levels of data. We then describe our sampling technique and the resulting coded data. Next, we suggest some directions for the future study of ethnicity and conflict using our bias-corrected data. Our preliminary correlations suggest selection bias may have distorted our understanding about both group and country correlates of ethnic violence.
In Journal of Conflict Resolution, 2018

Protracted conflicts over the status and demands of ethnic and religious groups have caused more instability and loss of human life than any other type of local, regional, and international conflict since the end of World War II. Yet we still have accumulated little in the way of accepted knowledge about the ethnic landscape of the world. In part this is due to empirical reliance on the limited data in the Minorities at Risk (MAR) project, whose selection biases are well known. In this article we tackle the construction of a list of ‘socially relevant’ ethnic groups meeting newly justified criteria in a dataset we call AMAR (A for All). We find that one of the principal difficulties in constructing the list is determining the appropriate level of aggregation for groups. To address this issue, we enumerate subgroups of the commonly recognized groups meeting our criteria so that scholars can use the subgroup list as one reference in the construction of the list of ethnic groups most appropriate for their study. Our conclusion outlines future work on the data using this expanded dataset on ethnic groups.
In Journal of Peace Research, 2015