Conflict Dataset Research Articles

When Does Audience Matter? Challengers’ Stability and Audience Costs

Abstract In this article, we argue that the degree to which audience costs work depends on the challenger state's leader stability. Unlike in the previous literature, we attempt to depart from a simple categorical comparison among regime types. Instead, we argue that the magnitude of audience costs varies continuously throughout the spectrum of a leader's stability. We employ probit regression with a squared term of stability, utilizing a large-N dataset of international conflicts provided by Militarized Interstate Dispute. Empirical results suggest a U-shaped curvilinear relationship between a challenger's stability and a target's probability of reciprocation. As the challenger's stability decreases, a target of interstate military conflict is less likely to reciprocate, that is, audience costs are more likely to be generated when a challenger is less stable. However, a target is less likely to concede as stability goes below a certain level because outside actors cannot learn the challenger's political patterns and intentions. Such findings imply that audience costs would be the highest at a moderate level of instability.

Varieties of post-civil war violence

Quantitative research on the “durability” of peace following civil wars typically captures the breakdown or survival of “peace” in a binary manner, equating it with the presence or absence of civil war recurrence. In the datasets that underpin such studies, years that do not experience full-scale civil war are implicitly coded as “peaceful.” Yet, post-civil war environments may remain free from war recurrence, while nevertheless experiencing endemic violent crime, state repression, low-intensity political violence, and systematic violence against marginalized groups, all of which are incongruent with the concept of peace. Approaches to assessing post-civil war outcomes which focus exclusively on civil war recurrence risk overestimating the “durability” of peace, implicitly designating as “peaceful” a range of environments which may be anything but. In this article, we discuss the heterogeneity of violent post-civil war outcomes and develop a typology of “varieties of post-civil war violence.” Our typology contributes to the study of post-civil war peace durability, by serving as the basis for an alternative, categorical conceptualization of “peace years” in conflict datasets.

Conflict heterogeneity in Africa

AbstractThe economic development literature widely concurs that conflicts have adverse economic consequences that contribute to poverty, disinvestment and lower human capital leading to widespread inequality and lower economic growth. As such, understanding the nature of conflict has been an important focus for political leaders, policymakers and researchers alike. However, the existing literature does not typically distinguish between the effects of conflict determinants on conflicts by type of actor or aggressor (i.e. state, group and civilian‐based). Using panel data analysis for 46 African countries from 1997 to 2017, and a comprehensive geo‐referenced Armed Conflict Location and Event Data (ACLED) conflict dataset, we find evidence of variation in the determinants' effects on conflicts by actor types. For the full sample of countries, we find that military expenditure decreases civilian‐based conflicts; globalisation increases both state‐ and civilian‐based conflicts while state fragility increases group‐based conflicts. On the other hand, income per capita increases all three types of conflicts. At regional level, we find variation in the effects of military expenditure and globalisation on state‐ and civilian‐based conflicts. However, we find little variation in the effects of the determinants on group‐based conflicts across the regions. The findings highlight the nuances in conflicts by actor types and their causes which need to be accounted for when formulating conflict resolution policies.

Money Talks: Cross-ethnic Patronage and Ethnic Conflict in China

AbstractElites within a particular minority out-group in autocracies typically exhibit fairly heterogeneous reactions to the ethnic group in power. The usual result is intra-group variations in the propensity to participate in ethnic conflict. To explain these within-group differences, I highlight the importance of cross-ethnic patronage, which refers to patronage allocated by the ethnic group in power to minority out-group elites. The state can use cross-ethnic patronage to mitigate the risk of ethnic conflicts through two mechanisms. First, the sharing of government spoils at stakes gives minority elites disincentives to initiate anti-regime mobilizations, and second, this co-optation approach effectively enlists cooperative minority elites to strengthen social control. I examine my theory using original county-level fiscal and personnel data of China's Xinjiang region, including annual payrolls and the ethnic composition of over 220,000 local bureaucrats. Findings based on a dataset of ethnic conflicts in Xinjiang from 1980 to 1995 support my theoretical arguments.

Introducing the Deadly Electoral Conflict Dataset (DECO)

This article introduces the Deadly Electoral Conflict dataset (DECO): a global, georeferenced event dataset on electoral violence with lethal outcomes from 1989 to 2017. DECO allows for empirical evaluation of theories relating to the timing, location, and dynamics of deadly electoral violence. By clearly distinguishing electoral violence from related (and sometimes concurrent) instances of organized violence, DECO is particularly suitable for investigating how election-related violence is connected to other forms of violent political contention. In the article, we present the theoretical and methodological underpinnings of the data collection and discuss empirical patterns that emerge in DECO. We also demonstrate one potential use of DECO by examining the association between United Nations peacekeeping forces and the prevalence of deadly electoral violence in conflict-affected countries.

A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collision danger between meeting ships. The number of collision accidents per unit time in a certain water area can be considered to describe the regional collision risk However, historical ship collision accidents have contingencies, small sample sizes and weak regularities; hence, ship collision conflicts can be used as a substitute for ship collision accidents in characterizing the maritime traffic safety situation and have become an important part of methods that quantitatively study the traffic safety problem and its countermeasures. In this work, an EMD-QPSO-LSSVM approach, which is a hybrid of empirical mode decomposition (EMD) and quantum-behaved particle swarm optimization (QPSO) optimized least squares support vector machine (LSSVM) model, is proposed to forecast ship collision conflicts. First, original ship collision conflict time series are decomposed into a collection of intrinsic mode functions (IMFs) and a residue with EMD. Second, both the IMF components and residue are applied to establish the corresponding LSSVM models, where the key parameters of the LSSVM are optimized by QPSO algorithm. Then, each subseries is predicted with the corresponding LSSVM. Finally, the prediction values of the original ship collision conflict datasets are calculated by the sum of the forecasting values of each subseries. The prediction results of the proposed method is compared with GM, Lasso regression method, EMD-ENN, and the predicted results indicate that the proposed method is efficient and can be used for the ship collision conflict prediction.

Strategic Samaritanism: how armed conflict affects aid receipts

ABSTRACT How does armed conflict affect the flow of foreign aid? Aid scholars have frequently argued that assistance is given either to serve donor interests or meet recipient needs and evaluated their theories using a single measure of aid. In this project we introduce the concept of an aid portfolio comprised of different types of assistance that a conflict-affected country might receive: humanitarian, economic, and aid to support governance and the social sector. We expect these components of a recipient country’s aid portfolio to vary according to both its needs and its capacity to absorb them. We call this logic, which assumes donors are motivated in part by considerations of efficacy, ‘strategic Samaritanism.’ We also expect conflict-affected countries’ needs and capacity to vary in predictable ways, with states that are severely affected by violence in need of more aid but less able to absorb some types of assistance until the fighting is over. Meanwhile, countries heavily affected by refugee flows should both need and be able to absorb more of each type of aid. We test our argument on a dataset of aid flows and violent conflicts in developing countries from 1989–2009 and find considerable supporting evidence for our expectations.

A reassessment of the impact of temperature change on European conflict during the second millennium CE using a bespoke Bayesian time-series model

Recently, there has been a lot of discussion about the impact of climate change on human conflict. Here, we report a study in which we revisited the findings of a paper that has been cited many times in the discussion. The paper in question focused on the association between temperature and conflict in Europe between 1000 and 1980 CE and suggested that colder temperatures led to more conflict. However, there are reasons to be skeptical of this finding. Most importantly, the analytical technique used by the paper’s authors was not suitable for the conflict dataset because the dataset is count-based and contains autocorrelation. With this in mind, we developed a Bayesian time-series model that is capable of dealing with these features, and then we reanalysed the dataset in conjunction with several temperature reconstructions. The results we obtained were unambiguous. None of the models that included temperature as a covariate outperformed a null hypothesis in which conflict levels at any given time were determined only by previous levels. Thus, we found no evidence that colder temperatures led to more conflict in Europe during the second millennium CE. When this finding is placed alongside the results of other studies that have examined temperature and conflict over the long term, it is clear that the impact of temperature on conflict is context dependent. Identifying the factor(s) that mediate the relationship between temperature and conflict should now be a priority.

Terrorism in armed conflict: new data attributing terrorism to rebel organizations

The Terrorism in Armed Conflict project integrates the Uppsala Conflict Data Project sample of rebel organizations with START’s Global Terrorism Database, covering 409 organizations for 1970–2013. For many Global Terrorism Database incidents, perpetrator information is missing, or ambiguous. Because the accuracy of perpetrator information likely varies systematically, simply dropping these incidents from analyses may bias results. Terrorism in Armed Conflict provides possible attribution to specific rebel groups with coding for uncertainty, enabling researchers to (1) address “description bias” in media-based terrorism data, (2) model uncertainty regarding perpetrator attribution and (3) vary the way terrorism is counted. The Terrorism in Armed Conflict dataset further provides a measure of deliberately indiscriminate terrorism that allows for more nuanced testing of arguments about the strategic logic of terrorism.

Conflict-Related Sexual Violence and the Re-Escalation of Lethal Violence

AbstractTo what extent does sexual violence influence the likelihood of re-escalating lethal violence after a period of inactivity? Despite a substantive body of research that explores conflict recurrence, the literature has largely neglected the role of rebel group dynamics. I address this gap arguing that reports of rebel sexual violence in periods deemed inactive because of low numbers of casualties are associated with greater risks of escalating lethal violence. Specifically, building on research that shows an association between recruitment and rape as a socialization method during civil war, I argue that reports of sexual violence indicate that rebels are maintaining and mobilizing fighters in inactive conflict years. I systematically test this argument on all inactive intrastate conflict years from 1989 to 2015 using the updated Sexual Violence in Armed Conflict dataset and Armed Conflict Termination dataset. The results provide robust support for the argument that a re-escalation of lethal violence following inactive periods is more likely when rebels are reported to perpetrate sexual violence in both active and subsequent inactive periods.

International Territorial Dispute Preferences in China and the United States

Within international relations theory, there is significant disagreement on the nature and significance of leaders’ dispute outcome preferences. While many variants of realism assume that such preferences are relatively fixed and homogeneous, both the liberal and the constructivist schools view them as significant variables. This debate remains unresolved because, for the standard large-sample conflict data sets, there are no direct measures of leadership preferences over outcomes in given types of international disputes. Using a conflict bargaining experiment, we ask whether, after controlling for the effects of relative power and initial conditions, leadership preferences have a statistically significant impact. We use two different country samples—from China and the United States—to examine whether the impact of leadership preferences varies internationally. We find that realist-style preferences are a special rather than a general case, and that such differences have significant implications for understanding continuities and changes in Chinese and US foreign policies.

How to Handle Armed Conflict Data in a Real-World Scenario?

Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time-consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus, our model contributes to the incorporation of technology in the peace building and conflict resolution sector.

Explaining Population Displacement Strategies in Civil Wars: A Cross-National Analysis

AbstractWhy do combatants uproot civilians in wartime? In this paper I identify cross-national variation in three population-displacement strategies—cleansing, depopulation, and forced relocation—and test different explanations for their use by state actors. I advance a new “assortative” theory to explain forced relocation, the most common type. I argue that combatants displace not only to expel undesirable populations, but also to identify the undesirables in the first place by forcing people to send signals of loyalty and affiliation based on whether, and to where, they flee. This makes communities more “legible” and facilitates the extraction of rents and recruits. I test these arguments using a novel Strategic Displacement in Civil Conflict data set (1945–2008). Consistent with my expectations, different displacement strategies occur in different contexts and appear to follow different logics. Cleansing is more likely in conventional wars, where territorial conquest takes primacy, while forced relocation is more likely in irregular wars, where identification problems are most acute. The evidence indicates that cleansing follows a logic of punishment. The results for relocation, however, are consistent with the implications of my assortative logic: it is more likely to be employed by resource-constrained incumbents fighting insurgencies in “illegible” areas—rural, peripheral territories. A case study from Uganda based on in-depth fieldwork provides evidence for the assortative mechanism. As the most comprehensive analysis of wartime displacement strategies to date, this paper challenges some core assumptions about a devastating form of contemporary political violence.

Refugees and violence: Introducing the Refugee-Related Security Incidence Dataset (RRSID)

Politicians regard refugees increasingly as a security threat. Yet, systematic knowledge on the types, locations and frequency of security incidences involving refugees is limited. While existing conflict datasets include refugees as actors and refugee settlements as event locations, they do not provide more micro-level data on violent events involving refugees. This article introduces a novel subnational geo-coded dataset on refugee-related security incidences that records all instances of violence in Central and Eastern Africa between 1999–2010 that take place within refugee settlements or involve refugees, independently of where it happened. Using the new data, we show that most events are sexual and gender-based violent events and occur among refugees, while security incidences involving rebel groups occur relatively seldom. We demonstrate in an analysis at the administrative unit level that different types of refugee accommodations, camps and self-settlements, are both associated with refugee-related violence. Hence, stakeholders urgently need to upscale the protection of refugees at these locations, particularly in Kenya where most incidences were recorded.

Re-describing transnational conflict in Africa

ABSTRACTThis paper discusses the principal findings of a new integrated dataset of transnational armed conflict in Africa. Existing Africa conflict datasets have systematically under-represented the extent of cross-border state support to belligerent parties in internal armed conflicts as well as the number of incidents of covert cross-border armed intervention and incidents of using armed force to threaten a neighbouring state. Based on the method of ‘redescribing’ datapoints in existing datasets, notably the Uppsala Conflict Data Project, the Transnational Conflict in Africa (TCA) data include numerous missing incidents of transnational armed conflict and reclassify many more. The data indicate that (i) trans-nationality is a major feature of armed conflict in Africa, (ii) most so-called ‘civil wars’ are internationalised and (iii) the dominant definitions of ‘interstate conflict’ and ‘civil war’ are too narrow to capture the particularities of Africa's wars. While conventional interstate war remains rare, interstate rivalry using military means is common. The dataset opens up a research agenda for studying the drivers, patterns and instruments of African interstate rivalries. These findings have important implications for conflict prevention, management and resolution policies.

Explaining Population Displacement Strategies in Civil Wars: A Cross National Analysis

Why do combatants uproot civilians in wartime? This paper identifies cross-national variation in three population displacement strategies – cleansing, depopulation, and forced relocation – and tests different explanations for their use by state actors. I advance a new “assortative” theory to explain forced relocation, the most common type. I argue that combatants displace not only to expel undesirable populations, but also to identify the undesirables in the first place by forcing people to send signals of loyalty and affiliation based on whether, and to where, they flee. This makes communities more “legible” and facilitates the extraction of rents and recruits. I test these arguments using a novel Strategic Displacement in Civil Conflict dataset (1945-2008). Consistent with my expectations, different displacement strategies occur in different contexts and appear to follow different logics. Cleansing is more likely in conventional wars, where territorial conquest takes primacy, while forced relocation is more likely in irregular wars, where identification problems are most acute. The evidence indicates that cleansing follows a logic of punishment. The results for relocation, however, are consistent with the implications of my assortative logic: it is more likely to be employed by resource-constrained incumbents fighting insurgencies in “illegible” areas – rural, peripheral territories. A case study from Uganda based on in-depth fieldwork provides evidence for the assortative mechanism. As the most comprehensive analysis of wartime displacement strategies to date, this paper challenges some core assumptions about a devastating form of contemporary political violence.

Ethnicity and Violent Conflicts in Northeast India: Analysing the Trends

ABSTRACTThis article is a moderate attempt to understand the various ideas associated with ethnicity and ethnic conflicts, and to study the nature, trends and typology of ethnic and insurgent conflicts in the North East Indian states (viz. Arunachal Pradesh, Assam, Meghalaya, Mizoram, Manipur, Nagaland and Tripura) from 1990 to 2016, using the UCDP/PRIO Armed Conflict Dataset. We find that ethnic and linguistic diversity does play a role in how violent conflicts by insurgent groups are carried out in the region; even though a mix of ethnic and socio-political/institutionalized nature may be attributed to it. Further, the gains from such conflict are often economic in nature.

Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field

Machine learning algorithms and artificial neural networks promise a new and powerful approach for making better and more transferable predictions in global conflict research. In this paper, a novel conflict dataset for the prediction of conflict intensity is introduced. It includes seven socio-economic and political indicators spanning a set of 851 country-years. This set of indicators is combined with conflict intensity data covering the timeframe of 2009–2015 to build a viable predictor framework. With this dataset as a foundation, a wide range of different predictive methods are tested, including linear discriminant analysis, classification and regression trees, k-nearest neighbor, random forest and several series of advanced artificial neural networks including a novel non-sequential long-short-term memory setup. Acknowledging the potential of deep learning techniques for many disciplines and projects, this paper shows, that for this type of assembled medium sized data, resembling many common research frameworks in Social and Political Sciences, using neural networks as singular approach might not be fruitful. The advantages of neural networks do not always outweigh their practical and technical disadvantages in small or medium data settings. The argument derived from this study is that researchers should combine Supervised Learning Algorithms and Deep Learning Networks as a general approach in similar predictive setups, or carefully evaluate for each dataset and project if the added complexity accompanied with using networks is indeed translating into better predictive performance.

Who Wins, Who Loses, Who Negotiates Peace in Civil Wars: Does Regime Type Matter?

AbstractPrevious research has shown that the outcome of a civil war is related to conflict duration: military victory by either the government or the rebels occurs early if it occurs at all, and the longer a civil war lasts, the more likely it is to end in a negotiated settlement. The models of civil war duration and outcome that have produced these findings are built on characteristics of the civil war and less on attributes of the state itself, other than where the state lies on the Polity autocracy-democracy scale. We propose that how civil wars end varies not only between democracies and authoritarian regimes but among the different authoritarian regime types identified by Geddes, Wright, and Franz. The distinguishing attributes of these regime types—democracy, one-party, personalist, military, monarchical—should lead to different likelihood in defeating a rebel movement, being defeated by a rebel movement, and negotiating a peace agreement with a rebel movement. Results from a series of competing-risk models using the Uppsala–Peace Research Institute Oslo Armed Conflict Dataset demonstrate support for our claim that how civil wars end is partly a function of the characteristics of the regime.

'Tis but thy name that is my enemy: On the construction of macro panel datasets in conflict and peace economics

The empirical analysis of datasets covering a large number of countries and time periods has become an integral part of conflict and peace economics. As such, numerous studies examine relationships between and among macroeconomic, political, and conflict variables and this often involves the merging of disparate datasets to combine relevant variables for which the country unit of analysis, however, is not necessarily the same. This article highlights difficulties in the data merging process and, by way of example, presents detailed country coding unit comparison for two economic (UN Comtrade and World Development Indicators), two democracy (Polity IV and V-Dem), and two conflict datasets (UCDP/PRIO Armed Conflict Dataset and COW Militarized Interstate Disputes Dataset). We find that merging datasets can result in the elimination of very large numbers of observations due to unmergeable records and that dropped observations often include the very countries or territorial entities most of interest in conflict and peace economics.