Suchergebnisse

After the collapse of the Soviet bloc, its twenty-seven successor states were charged with devising policies with respect to their ethnic minorities. This shock enables an analysis of the conditions that render states more likely to repress, exclude, assimilate or accommodate their minorities. One would anticipate that groups that are most 'threatening' to the state's territorial integrity are more likely to experience repression. However the data do not validate this expectation. Instead, the analysis suggests that minority groups' demographics and states' coercive capacities better account for variation in ethnic minority policies. While less robust, the findings further indicate the potential importance of lobby states and Soviet multinational legacies in determining minority rights. The results have implications for ethnic politics, human rights, nationalism, democratization and political violence. Adapted from the source document.

After the collapse of the Soviet bloc, its twenty-seven successor states were charged with devising policies with respect to their ethnic minorities. This shock enables an analysis of the conditions that render states more likely to repress, exclude, assimilate or accommodate their minorities. One would anticipate that groups that are most 'threatening' to the state's territorial integrity are more likely to experience repression. However the data do not validate this expectation. Instead, the analysis suggests that minority groups' demographics and states' coercive capacities better account for variation in ethnic minority policies. While less robust, the findings further indicate the potential importance of lobby states and Soviet multinational legacies in determining minority rights. The results have implications for ethnic politics, human rights, nationalism, democratization and political violence.

Why do insurgencies erupt in some places and not in others? This article exploits an original violent event database of 274,428 municipality-month observations in Colombia to determine the conditions favoring organized violence at the subnational level. The data cast doubt on the conventional correlates of war: poverty, rough terrain, lootable natural resources, and large, sparsely distributed populations. The evidence suggests that rebellions begin not in localities that afford sanctuaries, impoverished recruits, and abundant finances, but instead in regions providing receptacles of collective action: the organizational legacies of war. Specifically, the data indicate that regions affected by past mobilization are six times more likely to experience rebellion than those without a tradition of armed organized action. The significant correlation between prior and future mobilization is robust across different measurements of the concepts, levels of aggregations of the data, units of analysis, and specifications of the model. These include rare events and spatial lag analyses. These results highlight the need for micro conflict data, theory disentangling the causes of war onset from those of war recurrence, and a reorientation away from physical geography and back to the human and social geography that determines if rebellion is organizationally feasible. The findings suggest new avenues of research on the post-war trajectories of armed organizations, the causes of repeated war, and the micro-foundations of rebellion.

Abstract
Criminal war is a leading cause of death around the world. We argue for the inclusion of this topic in security studies and adapt a bargaining framework to shed light on why criminal groups fight or agree to peace. We propose that shocks to relative coercive capacity cause criminal war. This escalation in violent conflict proves more likely when criminal groups face greater difficulty negotiating: when they are more factionalized, less rooted in their territory, and in strategic rivalry with a greater number of rivals. Our empirical strategy leverages a critical, policy-relevant shock to access to weapons following an arms control repeal, and novel city block-level, monthly data on criminal organization traits, turf, and violence over ten years to understand how changes in coercive inputs upset the existing balance of power among criminal groups and shaped patterns of war and truce.

While ex-combatant reintegration is vital to successful transitions from war to peace, some former fighters turn to crime following demobilization. Such criminality undermines the consolidation of political order. Leading theories of crime participation emphasize the role of both individual economic opportunities and factors related to social ties. Yet, we still know little about the social logic of ex-combatant criminality and how social and economic factors relate as drivers of crime participation. This article presents a theory of how wartime social ties – namely, vertical ties to former commanders and horizontal ties to ex-combatant peers – influence ex-combatant crime on their own and via their relationship to economic opportunity costs. We use the theory to derive predictions in the context of Colombia, and then test them with a combination of administrative data and high-quality original survey data. We find that both vertical and horizontal wartime ties are powerful drivers of ex-combatant criminality. Our evidence indicates that wartime ties mitigate the risks of criminal behavior by facilitating the transmission of criminal capabilities and pro-crime social norms. We do not find that economic conditions moderate the effect of wartime times nor do we find any indication that economic opportunity costs, on their own, predict criminality. These findings underscore the importance of wartime ties – both vertical and horizontal – to understanding post-conflict transitions and designing reintegration interventions.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well-defined "retrospective intervention effect" based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.