Explaining Recruitment to Extremism: A Bayesian Case-Control Approach

TL;DR

This paper introduces a Bayesian case-control method inspired by epidemiology to analyze individual and contextual factors influencing extremism recruitment, addressing methodological challenges in studying rare events like joining ISIS.

Contribution

It develops a novel multilevel Bayesian approach combining survey and ecological data to identify factors associated with extremism recruitment, validated with ISIS fighters data.

Findings

High status, early twenties, university education linked to ISIS recruitment

Mixed evidence on the role of relative deprivation

Provides software for implementing the method

Abstract

Who joins extremist movements? Answering this question poses considerable methodological challenges. Survey techniques are infeasible and selective samples provide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we take inspiration from epidemiology and elaborate a technique that combines survey and ecological approaches. The multilevel Bayesian case-control design that we propose allows us to identify individual-level and contextual factors patterning the incidence of recruitment, while accounting for rare events, contamination, and spatial autocorrelation. We validate our approach by matching a sample of Islamic State (ISIS) fighters from nine MENA countries with representative population surveys enumerated shortly before recruits joined the movement. High status individuals in their early twenties with university education were more likely to join ISIS. There is more mixed evidence for relative deprivation. We provide software for applied researchers to implement our method.