Armed conflict and post-conflict justice, 1946–2006: A dataset

This article introduces a new dataset on post-conflict justice (PCJ) that provides an overview of if, where, and how post-conflict countries address the wrongdoings committed in association with previous armed conflict. Motivated by the literature on post-conflict peacebuilding, we study justice processes during post-conflict transitions. We examine: which countries choose to implement PCJ; where PCJ is implemented; and which measures are taken in post-conflict societies to address past abuse. Featuring justice and accountability processes, our dataset focuses solely on possible options to address wrongdoings that are implemented following and relating to a given armed conflict. These data allow scholars to address hypotheses regarding justice following war and the effect that these institutions have on transitions to peace. This new dataset includes all extrasystemic, internationalized internal, and internal armed conflicts from 1946 to 2006, with at least 25 annual battle-related deaths as coded by the UCDP/PRIO Armed Conflict Dataset. The post-conflict justice (PCJ) efforts included are: trials, truth commissions, reparations, amnesties, purges, and exiles. By building upon the UCDP/PRIO Armed Conflict Dataset, scholars interested in PCJ can include variables regarding the nature of the conflict itself to test how PCJ arrangements work in different environments in order to better address the relationships between justice, truth, and peace in the post-conflict period.

All Conflict is Local: Modeling Sub-National Variation in Civil Conflict Risk

Most quantitative assessments of civil conflict draw on annual country-level data to determine a baseline hazard of conflict onset. The first problem with such analyses is that they ignore factors associated with the precipitation of violence, such as elections and natural disasters and other trigger mechanisms. Given that baseline hazards are relatively static, most of the temporal variation in risk is associated with such precipitating factors. The second problem with most quantitative analyses of conflict is that they assume that civil conflicts are distributed uniformly throughout the country. This is rarely the case; most intrastate armed conflicts take place in the periphery of the country, well away from the capital and often along international borders. Analysts fail to disaggregate temporally as well as spatially. While other contributions to this issue focus on the temporal aspect of conflict, this article addresses the second issue: the spatial resolution of analysis. To adequately assess the baseline risk of armed conflict, this article develops a unified prediction model that combines a quantitative assessment of conflict risk at the country level with country-specific sub-national analyses at first-order administrative regions. Geo-referenced data on aspects of social, economic, and political exclusion, as well as endemic poverty and physical geography, are featured as the principal local indicators of latent conflict. Using Asia as a test case, this article demonstrates the unique contribution of applying a localized approach to conflict prediction that explicitly captures sub-national variation in civil conflict risk.