The Categorically Disaggregated Conflict (CDC) Dataset

The Categorically Disaggregated Conflict (CDC) Dataset provides a categorization of 331 intrastate armed conflicts recorded between 1946 and 2010 into four categories: 1. Ethnic governmental; 2. Ethnic territorial; 3. Non-ethnic governmental; 4. Non-ethnic territorial. The dataset uses the UCDP/PRIO Armed Conflict Dataset v.4-2011, 1946 – 2010 (Themnér & Wallensteen, 2011; also Gleditsch et al., 2002) as a base (and thus is an extension of the UCDP/PRIO dataset). The CDC dataset has been presented in Bartusevicius (2016) (see citation below). A copy of the abstract from the article: Conflict researchers have increasingly stressed the importance of distinguishing between different categories of civil conflict, such as ethnic vs non-ethnic. However, the data on conflict categories has remained limited. This paper introduces the Categorically Disaggregated Conflict (CDC) dataset, which categorizes conflicts based on the two most commonly used distinctions, ethnic-vs-non-ethnic and governmental-vs-territorial, resulting in four conflict categories: ethnic governmental, ethnic territorial, non-ethnic governmental and non-ethnic territorial. While not the first of its kind, the CDC contains a number of novel features. Aside from its unique conceptualization of ethnic conflict, the CDC provides coding of the key component variables (language, religion and “race”), allowing users to re-code ethnic/non-ethnic conflicts into several alternative lists (e.g. religious/non-religious). Furthermore, the CDC provides detailed descriptions documenting coding choices for every single conflict, allowing users to track individual coding decisions. To demonstrate the value of the CDC, this paper replicates a recent study by Cederman, Gelditsch and Buhaug, based on the ACD2EPR—the only extant alternative to the CDC. The findings of the replication analysis challenge some of the key conclusions of the original study, substantiating the need for alternative categorically disaggregated datasets.