Financing rebellion

Abstract

A prominent explanation of the resource–conflict relationship suggests that natural resources finance rebellion by permitting rebel leaders the opportunity to purchase weapons, fighters, and local support. The bunkering of oil in the Niger Delta by quasi-criminal syndicates is an example of how the black-market selling of stolen oil may help finance anti-state groups. More systematic assessments have also shown that the risk and duration of conflict increases in the proximity of oil and diamond deposits. Yet despite the emphasis on rebel resource extraction in these arguments, empirical assessments rely almost exclusively on latent resource availability rather than actual resource extraction. Focusing on maritime piracy, this article argues that piracy is a funding strategy neglected in current research. Anecdotal evidence connects piracy in the Greater Gulf of Aden to arms trafficking, the drug trade, and human slavery. The revenue from attacks may find its way to Al-Shabaab. In Nigeria, increasing attacks against oil transports may signal an effort by insurgents to use the profits from piracy as an additional revenue stream to fund their campaign against the Nigerian government. The article hypothesizes that piracy incidents, that is, actual acts of looting, increase the intensity of civil conflict. Using inferential statistics and predictive assessments, our evidence from conflicts in coastal African and Southeast Asian states from 1993 to 2010 shows that maritime piracy increases conflict intensity, and that the inclusion of dynamic factors helps improve the predictive performance of empirical models of conflict events in in-sample and out-of-sample forecasts.

The Politics and Science of Prevision

“…Increasingly, scholars also aim to go beyond binary outcomes and instead predict the level or intensity of violence (e.g. Bagozzi 2015; van Weezel 2016;Daxecker and Prins 2017b). Finally, conflict scholars also make predictions of more positive or at least non-violent outcomes, such as the start of non-violent resistance (Chenoweth and Ulfelder 2017), the onset and success of mediation (Clayton and Gleditsch 2014), ceasefire success (Schneider et al 2017), quality of domestic governance (Joshi et al 2015), or the international community's conflict management behaviour in international disputes (Owsiak 2015).…”

Forecasting peace agreement content: How conflict events predict the substance of peace settlements

“…As mentioned earlier, a second interest of this paper lies in the methodological debate between supporters of classical methods (Blair and Sambanis, 2020;Chiba and Gleditsch, 2017;Weidmann and Ward, 2010;Daxecker and Prins, 2017;Hirose et al, 2017) and supporters of more modern machine learning methods (Beger et al, 2021;Dorff et al, 2022;D'Orazio et al, 2019;Hegre et al, 2019;Malone, 2022;Hultman et al, 2022;Mueller and Rauh, 2022). Modern machine learning methods often fare better at prediction than simpler models.…”

“…Nevertheless, prediction has received a lot of attention in peace research more generally, with a particular focus on forecasting conflict onset and civil war violence. However, there has been a ubiquitous divide between studies that use more classical structural methods (Chiba and Gleditsch, 2017;Weidmann and Ward, 2010;Daxecker and Prins, 2017;Hirose et al, 2017) and a growing number that use different types of machine learning (Dorff et al, 2022;D'Orazio et al, 2019;Hegre et al, 2019;Malone, 2022;Hultman et al, 2022;Mueller and Rauh, 2022). Usually, the respective choice in method is driven by the authors' goals.…”

Computational Conflict Research

“…For example, in the Niger Delta region of Nigeria, local rebel groups kidnap corporate employees and release them in exchange for ransom (Ikelegbe, 2006). Revenues obtained through piracy also often end up in rebel pockets (Daxecker and Prins, 2017). Business elites in Somalia are required by local warlords to pay road taxes in addition to periodical payments in exchange for security (Ahmad, 2015).…”