The Effect of the Covid-19 Pandemic on Global Armed Conflict: Early Evidence

As Covid-19 spreads around the world, international actors, including the United Nations, have called for a stop to armed conflict to facilitate efforts to fight the pandemic. At the same time, coronavirus may also trigger and intensify armed conflict due to its negative economic consequences and by offering windows of opportunity to opposition movements to attack distracted and weakened incumbents. We use real-time data on the spread of Covid-19, governmental lockdown policies, and battle events to study the causal short-term effect of the pandemic on armed conflict. Our results suggest that both the spread of Covid-19 and lockdown policies exhibit a global Null effect with considerable regional heterogeneity. Most importantly, governmental lockdowns have increased armed conflict in the Middle East. In contrast, reported combat has decreased in Southeast Asia and the Caucasus as the pandemic has spread

All that glitters is not gold: Relational events models with spurious events

As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events