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What Drives State-Sponsored Violence?: Evidence from Extreme Bounds Analysis and Ensemble Learning Models

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  • Freire, Danilo

    (Brown University)

  • Uzonyi, Gary

Abstract

The literature on state-sponsored violence has grown significantly over the last decades. Although scholars have suggested a number of potential correlates of mass killings, it remains unclear whether the estimates are robust to different model specifications, or which variables accurately predict the onset of large-scale violence. We employ extreme bounds analysis and distributed random forests to test the sensitivity of 40 variables on a sample of 177 countries from 1945 to 2013. The results help clear the brush around mass killings, as few variables in this literature are robust determinants of atrocity. However, support for an opportunity logic persists as greater constraints on a government limit its ability to employ barbarous tactics. It appears that the Conflict Trap applies to government atrocity. Atrocity breeds atrocity, while wealthy stable democracies tend to avoid episodes of mass killing.

Suggested Citation

  • Freire, Danilo & Uzonyi, Gary, 2018. "What Drives State-Sponsored Violence?: Evidence from Extreme Bounds Analysis and Ensemble Learning Models," SocArXiv pzx3q, Center for Open Science.
  • Handle: RePEc:osf:socarx:pzx3q
    DOI: 10.31219/osf.io/pzx3q
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    References listed on IDEAS

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    Cited by:

    1. Freire, Danilo, 2021. "Democratizing Policy Analytics with AutoML," Working Papers 11015, George Mason University, Mercatus Center.

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