Ethnic Power Relations (EPR) Dataset Family 2021
The EPR Dataset Family provides data on ethnic groups’ access to state power, their settlement patterns, links to rebel organizations, transborder ethnic kin relations, and intraethnic cleavages. The 2014 version has been introduced in Vogt, Bormann, Rüegger, Cederman, Hunziker, Girardin (2015) and has been updated in 2021 in a series of data sets on ethnicity that have stimulated civil war research in the past decade. It features a comprehensive system of tightly integrated data sets:
The EPR Core dataset identifies all politically relevant ethnic groups and their access to state power in every country of the world from 1946 to 2021. It includes annual data on over 800 groups and codes the degree to which their representatives held executive-level state power—from total control of the government to overt political discrimination.
The GeoEPR dataset provides geo-spatial information about every politically relevant ethnic group. It assigns to each ethnic group one of six settlement patterns and, if relevant, provides polygons describing their location on a digital map.
The ACD2EPR docking dataset links conflicts inventoried in UCDP/PRIO Armed Conflict Dataset to politically relevant ethnic groups.
The Transborder Ethnic Kin (EPR-TEK) dataset records all politically relevant ethnic groups living in at least two countries, i.e. ethnic groups with transnational ethnic connections and whose settlement area is split by an international border.
The Ethnic Dimensions (EPR-ED) dataset provides information on the linguistic, religious, and racial cleavages that characterize and internally divide the politically relevant ethnic groups.
The Ethnicity of Refugees (EPR-ER) dataset records the ethnic composition of refugee stocks worldwide for the years 1975 to 2020.
The EPR Dataset Family is complemented by the EPR Aggregate Group (EPR-AG) data.
Download
The current version of the EPR dataset family is available in research-ready country-year and group-year format from the GROWup Research Front-End data portal.
You may also download the latest version (2021, released on June 8, 2021) of the raw EPR component datasets directly in CSV (UTF-8 encoded), JSON, Excel (.xls), Excel 2007 OOXML (.xlsx), Shapefile, and SQL formats:
Codebook | CSV | Tab-delimited | JSON | Excel | Excel 2007 | Shapefile | SQL | |
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EPR Core Dataset 2021 | ||||||||
GeoEPR Dataset 2021 | ||||||||
ACD2EPR Dataset 2021 | ||||||||
EPR-TEK Dataset 2021 | ||||||||
EPR-ED Dataset 2021 | ||||||||
EPR-ER Dataset 2021 |
Citation
When using these datasets in your research, please cite the following publication:
When using EPR-ED in your research, please include the following reference:
When using EPR-ER in your research, please include the following reference:
When referring to the original dataset EPR v. 1.1 please cite:
When referring to the original GeoEPR v. 2.0 dataset please cite:
When referring to the original ACD2EPR v. 1.2 dataset please cite:
Team
Contact
For general enquiries about the EPR Dataset Family, please contact Luc Girardin.
Current
Lars-Erik Cederman, Luc Girardin, Seraina Rüegger, Guy Schvitz
Former members
Nils-Christian Bormann, Kristian S. Gleditsch, Philipp Hunziker, Nils Metternich, Carl Müller-Crepon, Francisco Villamil, Manuel Vogt Nils B. Weidmann Julian Wucherpfennig
Country experts and GIS coders
Nino Abzianidze, Daniela Arauz, Dennis Atzenhofer, Corinne Bara, Emily A. Beaulieu, Sandra Berger, Heidrun Bohnet, Nils-Christian Bormann, Camiel Boukhaf, Johan Brosche, Ana Maria Burgos, Fabien Cottier, Sarah Däscher, Lukas Dick, Benjamin Füglister, Naomi Furnish Yamada, Alina Gäumann, Armando Geller, Eloise Harris, Jacqueline Heinzelmann, Mirjam Hirzel, Andreas Juon, Julia Karpati, Nora Keller, Vanessa Kellerhals, Fabiana Koller, Davina Krumbholz, Esther Leeman, Theresa Leimpek, Dan Miodownik, Fabian Morgenthaler, Carl Müller-Crepon, Radhika Nagesh, Simon Neuland, Lirije Palushi, Yannick Pengl, Simon Pressler, Blerina Rexha, Seraina Rüegger, Andi Schädel, Nadja Schloss, Nora Schmidlin, Guy Schvitz, Irina Siminichina, Nanya Sudhir, Edina Szöcsik, Paola Galano Toro, Jessica Trisko Darden, Margot van den Bergh, Francisco Villamil, Manuel Vogt, Lorraine Wong, Toru Yamada
This research has benefited from generous financial support from the Swiss National Science Foundation (Grants 100017_156339 & 400240_171175) and an Advanced ERC Grant 787478 NASTAC.