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Temporally and spatial disaggregated datasets are commonly used to study political violence. Researchers are increasingly studying the data generation process itself to understand the selection processes by which conflict events are included in conflict datasets. This work has focused on conflict fatalities. In this research note, we explore how non-fatal conflict events are reported upon and enter into datasets of armed conflict. To do so, we compare reported non-fatal conflict events with the population of events in two direct observation datasets, collected using a boots-on-the-ground strategy: mass abductions in Nepal (1996–2006) and troop movements in Darfur. We show that at the appropriate level of aggregation media reporting on abductions in Nepal largely mirrors the "true" population of abductions, but at more disaggregated levels of temporal or spatial analysis, the match is poor. We also show that there is no overlap between a media-driven conflict dataset and directly-observed data on troop movements in Sudan. These empirics indicate that non-fatal data can suffer from serious underreporting and that this is particularly the case for events lacking elements of coercion. These findings are indicative of selection problems in regards to the reporting on non-fatal conflict events.

The spread of Internet and mobile phone access around the world has implications for both the processe s of contentious politics and subsequent reporting of protest, terrorism, and war. In this paper, we explore whether political violent events that occur close to modern communication networks are systematically better reported than others. Our analysis app roximates information availability by the level of detail provided about the date of each political violent event in Africa from 2008 to 2010 and finds that although access to communication technology improves reporting, the size of the effect is very smal l. Additional investigation finds that the effect can be attributed to the ability of journalists to access more diverse primary sources in remote areas due to increased local access to modern communication technology.

The spread of Internet and mobile phone access around the world has implications for both the processes of contentious politics and subsequent reporting of protest, terrorism, and war. In this paper, we explore whether political violent events that occur close to modern communication networks are systematically better reported than others. Our analysis approximates information availability by the level of detail provided about the date of each political violent event in Africa from 2008 to 2010 and finds that although access to communication technology improves reporting, the size of the effect is very small. Additional investigation finds that the effect can be attributed to the ability of journalists to access more diverse primary sources in remote areas due to increased local access to modern communication technology.

The spread of Internet and mobile phone access around the world has implications for both the processes of contentious politics and subsequent reporting of protest, terrorism, and war. In this paper, we explore whether political violent events that occur close to modern communication networks are systematically better reported than others. Our analysis approximates information availability by the level of detail provided about the date of each political violent event in Africa from 2008 to 2010 and finds that although access to communication technology improves reporting, the size of the effect is very small. Additional investigation finds that the effect can be attributed to the ability of journalists to access more diverse primary sources in remote areas due to increased local access to modern communication technology.

Manual coding of political events from news reports is extremely expensive and time-consuming, whereas completely automatic coding has limitations when it comes to the precision and granularity of the data collected. In this paper, we introduce an alternative strategy by establishing a semi-automatic pipeline, where an automatic classification system eliminates irrelevant source material before further coding is done by humans. Our pipeline relies on a high-performance supervised heterogeneous ensemble classifier working on extremely unbalanced training classes. Deployed to the Mass Mobilization on Autocracies database on protest, the system is able to reduce the number of source articles to be human-coded by more than half, while keeping over 90% of the relevant material.

Manual coding of political events from news reports is extremely expensive and time-consuming, whereas completely automatic coding has limitations when it comes to the precision and granularity of the data collected. In this paper, we introduce an alternative strategy by establishing a semi-automatic pipeline, where an automatic classification system eliminates irrelevant source material before further coding is done by humans. Our pipeline relies on a high-performance supervised heterogeneous ensemble classifier working on extremely unbalanced training classes. Deployed to the Mass Mobilization on Autocracies database on protest, the system is able to reduce the number of source articles to be human-coded by more than half, while keeping over 90% of the relevant material.

In this article, we present the most up-to-date, fine-grained, global dataset on external support in armed conflicts: the UCDP External Support Dataset (ESD). The dataset encompasses data on states and non-state actors as both supporters and recipients and provides detailed information on the type of support provided to warring parties in armed conflicts between 1975 and 2017. We use it to highlight three broader trends in the provision of external support: (1) a dramatic increase in the number of external supporters, (2) a larger share of pro-government interventions, and (3) the rise of direct military intervention as the predominant mode of external support. In conclusion, we identify several avenues worthy of future inquiry that could significantly improve our understanding of external support in armed conflicts.

In this article, we present the most up-to-date, fine-grained, global dataset on external support in armed conflicts: the UCDP External Support Dataset (ESD). The dataset encompasses data on states and non-state actors as both supporters and recipients and provides detailed information on the type of support provided to warring parties in armed conflicts between 1975 and 2017. We use it to highlight three broader trends in the provision of external support: (1) a dramatic increase in the number of external supporters, (2) a larger share of pro-government interventions, and (3) the rise of direct military intervention as the predominant mode of external support. In conclusion, we identify several avenues worthy of future inquiry that could significantly improve our understanding of external support in armed conflicts.

Although substantive agreement exists on the role of climate variability and food scarcity in increasing violence, a limited number of studies have investigated how food resources affect violent conflict. This article explores the complex linkages between climate variability, agricultural production and conflict onset, by focusing on the spatial distribution of crop production in a cross-country setting. We hypothesize that spatial differences in crop production within countries are a relevant factor in shaping the impact of climate variability on conflict in agriculturally -dependent countries. To test this hypothesis, we rely on high-resolution global gridded data on the local yield of four main crops for the period 1982–2015 and aggregate the grid-cell information on crop production to compute an empirical indicator of the spatial concentration of agricultural production within countries. Our results show that the negative impacts of climate variability lead to an increase in the spatial concentration of agricultural production within countries. In turn, the combined effect of climate extremes and crop production concentration increases the predicted probability of conflict onset by up to 14% in agriculturally dependent countries.

Although substantive agreement exists on the role of climate variability and food scarcity in increasing violence, a limited number of studies have investigated how food resources affect violent conflict. This paper explores the complex linkages between climate variability, agricultural production and conflict onset, by focusing on the spatial distribution of crop production in a cross-country setting. We hypothesize that spatial differences in crop production within countries are a relevant factor in shaping the impact of climate variability on conflict in agriculturally-dependent countries. To test this hypothesis, we rely on high-resolution global gridded data on the local yield of four main crops for the period 1982-2015 and aggregate the grid-cell information on crop production to compute an empirical indicator of the spatial concentration of agricultural production within countries. Our results show that the negative impacts of climate variability lead to an increase in the spatial concentration of agricultural production within countries. In turn, the combined effect of climate extremes and crop production concentration increases the predicted probability of conflict onset by up to 14% in agriculturally-dependent countries.

This article presents a new, monthly updated dataset on organized violence—the Uppsala Conflict Data Program Candidate Events Dataset. It contains recent observations of candidate events, a majority of which are eventually included in the Uppsala Conflict Data Program Georeferenced Event Dataset as part of its annual update after a careful vetting process. We describe the definitions, sources and procedures employed to code the candidate events, and a set of issues that emerge when coding data on organized violence in near-real time. Together, the Uppsala Conflict Data Program Candidate and Georeferenced Event Datasets minimize an inherent trade-off between update speed and quality control. Having monthly updated conflict data is advantageous for users needing near-real time monitoring of violent situations and aiming to anticipate future developments. To demonstrate this, we show that including them in a conflict forecasting system yields distinct improvements in terms of predictive performance: Average precision increases by 20–40% relative to using the Uppsala Conflict Data Program Georeferenced Event Dataset only. We also show that to ensure quality and consistency, revisiting the initial coding making use of sources that become available later is absolutely necessary.

This article presents a new, monthly updated dataset on organized violence—the Uppsala Conflict Data Program Candidate Events Dataset. It contains recent observations of candidate events, a majority of which are eventually included in the Uppsala Conflict Data Program Georeferenced Event Dataset as part of its annual update after a careful vetting process. We describe the definitions, sources and procedures employed to code the candidate events, and a set of issues that emerge when coding data on organized violence in near-real time. Together, the Uppsala Conflict Data Program Candidate and Georeferenced Event Datasets minimize an inherent trade-off between update speed and quality control. Having monthly updated conflict data is advantageous for users needing near-real time monitoring of violent situations and aiming to anticipate future developments. To demonstrate this, we show that including them in a conflict forecasting system yields distinct improvements in terms of predictive performance: Average precision increases by 20–40% relative to using the Uppsala Conflict Data Program Georeferenced Event Dataset only. We also show that to ensure quality and consistency, revisiting the initial coding making use of sources that become available later is absolutely necessary.

Common political economy models point to rationalist motives for engaging in conflict but say little about how income shocks translate into collective violence in some cases but not in others. Grievance models, in contrast, focus on structural origins of shared frustration but offer less insight into when the deprived decide to challenge the status quo. Addressing these lacunae, we develop a theoretical model of civil conflict that predicts income loss to trigger violent mobilization primarily when the shock can be linked to pre-existing collective grievances. The conditional argument is supported by results of a comprehensive global statistical analysis of conflict involvement among ethnic groups. Consistent with theory, we find that this relationship is most powerful among recently downgraded groups, especially in the context of agricultural dependence and low local level of development, whereas political downgrading in the absence of adverse economic changes exerts less influence on ethnic conflict risk.

Common political economy models point to rationalist motives for engaging in conflict but say little about how income shocks translate into collective violence in some cases but not in others. Grievance models, in contrast, focus on structural origins of shared frustration but offer less insight into when the deprived decide to challenge the status quo. Addressing these lacunae, we develop a theoretical model of civil conflict that predicts income loss to trigger violent mobilization primarily when the shock can be linked to preexisting collective grievances. The conditional argument is supported by results of a comprehensive global statistical analysis of conflict involvement among ethnic groups. Consistent with theory, we find that this relationship is most powerful among recently downgraded groups, especially in the context of agricultural dependence and low local level of development, whereas political downgrading in the absence of adverse economic changes exerts less influence on ethnic conflict risk. ; publishedVersion