ARC is the first systematic dataset to code how rebel organizations recruit — across nine dimensions, from gender and age diversity to military background. Explore 319 armed groups across 66 countries, active since 2000.
Share of groups exhibiting each characteristic. Groups with missing data on a given dimension are excluded from its denominator. Click a bar to filter to groups with that trait.
Each glyph is a group's recruitment "fingerprint" — 9 segments represent the 9 dimensions. Blue = Yes, dim = No, faint = No data. Score shown in center. Hover to identify; click to explore.
Each line is one rebel group crossing all 9 dimensions. Lines that cluster toward the top recruited broadly on that dimension; those at the bottom did not. Color by primary ideology to reveal patterns.
The composite score (0–9) sums how many of the nine dimensions a group scores YES on. Limited to fully-coded groups; partially-coded groups are excluded to avoid distortion. Hover a bar to see the groups in that bin.
Highest and lowest diversity scorers, plus a well-known group for reference. Click to explore their full profiles.
Click any column header to sort. Click a row to open its full profile. All active filters apply.
The Armed Group Composition (ARC) dataset provides the first systematic, cross-national data on rebel group recruitment patterns. For each group, ARC codes whether members were recruited with diversity across nine dimensions: gender, age, education, intra-country origin, extra-country origin, language, plus prior experience in irregular military, regular military, and police.
ARC covers 319 armed groups drawn from the Armed Group Dataset, active since 2000 with formation dates ranging from 1941 to 2012. Groups active in multiple countries appear once per country. A composite Recruitment Diversity Score (0–9) sums positive codings across all nine dimensions.
For methodology, variable definitions, and the full replication archive, see the data release article.