1,721,013 research outputs found
Replication Data for: On Malaria and the Duration of Civil War
No full textNecessary replication files for: Bagozzi, Benjamin E. 2016. "On Malaria and the Duration of Civil War." Journal of Conflict Resolution. 60(5): 813-839
Replication Data for: On Malaria and the Duration of Civil War
No full textNecessary replication files for: Bagozzi, Benjamin E. 2016. "On Malaria and the Duration of Civil War." Journal of Conflict Resolution. 60(5): 813-839
Replication Data for: The Stabilizing Effects of International Politics on Bilateral Trade Flows
No full textReplication data for the following article:
Bagozzi, Benjamin E. and Steven T. Landis. 2015. "The Stabilizing Effects of International Politics on Bilateral Trade Flows." Foreign Policy Analysis. 11(2):151-171
Replication Data for: The Stabilizing Effects of International Politics on Bilateral Trade Flows
No full textReplication data for the following article:
Bagozzi, Benjamin E. and Steven T. Landis. 2015. "The Stabilizing Effects of International Politics on Bilateral Trade Flows." Foreign Policy Analysis. 11(2):151-171
Replication Data for: Forecasting Civil Conflict with Zero-Inflated Count Models
No full textAdvances in the study of civil war have led to the proliferation of event count data, and to a corresponding increase in the use of (zero-inflated) count models for the quantitative analysis of civil conflict events. Our ability to effectively use these techniques is met with two current limitations. First, researchers do not yet have a definitive answer as to whether zero-inflated count models are a verifiably better approach to civil conflict modeling than are ‘less assuming’ approaches such as negative binomial count models. Second, the accurate analysis of conflict-event counts with count models –zero-inflated or otherwise – is severely limited by the absence of an effective framework for the evaluation of predictive accuracy, which is an empirical approach that is of increasing importance to conflict modelers. This article rectifies both of these deficiencies. Specifically, this study presents count forecasting techniques for the evaluation and comparison of count models’ predictive accuracies. Using these techniques alongside out-of-sample forecasts, it then definitively verifies – for the first time – that zero-inflated count models are superior to comparable non-inflated models for the study of intrastate conflict events
Replication Data for: The Multifaceted Nature of Global Climate Change Negotiations.
No full textThe attached files contain all necessary replication materials for Bagozzi, B.E. 2015. "The Multifaceted Nature of Global Climate Change Negotiations.'' Review of International Organizations. 10(4): 439-464
Replication Data for: The Multifaceted Nature of Global Climate Change Negotiations.
No full textThe attached files contain all necessary replication materials for Bagozzi, B.E. 2015. "The Multifaceted Nature of Global Climate Change Negotiations.'' Review of International Organizations. 10(4): 439-464
Replication Data for: Forecasting Civil Conflict with Zero-Inflated Count Models
No full textAdvances in the study of civil war have led to the proliferation of event count data, and to a corresponding increase in the use of (zero-inflated) count models for the quantitative analysis of civil conflict events. Our ability to effectively use these techniques is met with two current limitations. First, researchers do not yet have a definitive answer as to whether zero-inflated count models are a verifiably better approach to civil conflict modeling than are ‘less assuming’ approaches such as negative binomial count models. Second, the accurate analysis of conflict-event counts with count models –zero-inflated or otherwise – is severely limited by the absence of an effective framework for the evaluation of predictive accuracy, which is an empirical approach that is of increasing importance to conflict modelers. This article rectifies both of these deficiencies. Specifically, this study presents count forecasting techniques for the evaluation and comparison of count models’ predictive accuracies. Using these techniques alongside out-of-sample forecasts, it then definitively verifies – for the first time – that zero-inflated count models are superior to comparable non-inflated models for the study of intrastate conflict events
Replication Data for: From Global to Local, Food Insecurity is Associated with Contemporary Armed Conflicts
No full textFood security has attracted widespread attention in recent years. Yet, scientists and practitioners have predominately understood food security in terms of dietary energy availability and nutrient deficiencies, rather than in terms of food security’s consequential implications for social and political violence. The present study offers the first global evaluation of the effects of food insecurity on local conflict dynamics. An economic approach is adopted to empirically evaluate the degree to which food insecurity concerns produce an independent effect on armed conflict using comprehensive geographic data. Specifically, two agricultural output measures – a geographic area’s extent of cropland and a given agricultural location’s amount of cropland per capita – are used to respectively measure the access to and availability of (i.e., the demand and supply of) food in a given region. Findings show that food insecurity measures are robustly associated with the occurrence of contemporary armed conflict
Distinguishing Occasional Abstention from Routine Indifference in Models of Vote Choice
No full textResearchers commonly employ multinomial logit (MNL) models to explain individual level vote choice while treating “abstention” as the baseline category. Though many view abstainers as a homogeneous group, we argue that these respondents emerge from two distinct sources. Some nonvoters are likely to be “occasional voters” who abstained from a given election due to temporary factors, such as a distaste for all candidates running in a particular election, poor weather conditions, or other temporary circumstances. On the other hand, many nonvoters are unlikely to vote regardless of the current political climate. This latter population of “routine nonvoters” is consistently disengaged from the political process in a way that is distinct from that of occasional voters. Including both sets of nonvoters within an MNL model can bias the estimated effects of variables on candidate-selection or turnout, leading to faulty inferences. As a solution, we propose a baseline-inflated MNL estimator that models heterogeneous populations of nonvoters probabilistically, thus accounting for the presence of routine nonvoters within models of vote choice. We demonstrate the utility of this model using replications of existing public opinion research
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