Sage Journals: Discover world-class research

Abstract

Event datasets, such as those provided by the Uppsala Conflict Data Program (UCDP), provide high-quality data on conflict fatalities. However, such data are likely to suffer from an unknown extent of bias and uncertainties in the reports they are based on. Although a substantial literature documents reporting bias in conflict research, analyses that quantify this bias are mostly limited to single countries. Here, we combine a survey with UCDP coders and statistical modeling to derive a distribution of plausible number of fatalities given the number of battle-related deaths and the type of violence documented by the UCDP. We provide a generalizable, cross-national measure of uncertainty around UCDP reported fatalities that is more robust and realistic than UCDP’s documented low and high estimates, countering UCDP’s intrinsic tendency to under-estimate fatalities, and we make available a dataset and R package that can be applied to future releases of the UCDP data.

Keywords

armed conflict conflict data measurement uncertainty expert survey

Introduction

Empirical research on peace and conflict has increasingly relied on event datasets that provide information on armed conflict events and related deaths for multiple countries and over time. The majority of these datasets are based on automatic or manual coding of events from news and media sources, thus giving rise to potential reporting bias. Although this bias and its implications for conflict research are largely acknowledged, there is currently no systematic approach to quantify uncertainty about reported conflict fatalities, nor solutions to handle measurement error.

There are two types of uncertainty and bias in conflict event datasets. One relates to the unknown and uncertain events that fail to be reported at all in the available sources; the other, to bias and uncertainty in the events that are actually reported by credible news sources, government and NGO reports, etc. Most of existing research has focused on the former. In this article, we focus on the latter, and provide the first systematic modeling of uncertainty in armed conflict fatalities reported by the Uppsala Conflict Data Program (UCDP).

UCDP is one of the most acknowledged and widely used among conflict event datasets, reporting organized violence globally since 1989. The UCDP data are hand-collected based on a rigorous and immutable definition of armed conflict, and systematic and transparent coding rules, which make them comparable across cases and countries, and over time (Davies et al. 2025; Sundberg and Melander 2013). UCDP relies on local, national, and international news sources, as well as IGO, NGO and research centers’ monitoring efforts. The UCDP has unrivaled experience and allocates considerable resources to assess reporting bias, using multiple sources for each event wherever feasible. Still, some bias is unavoidable. UCDP has a known tendency to be conservative when uncertain, but little is known about the amount of this bias, as the UCDP does not provide a simple, consistent estimate of their own uncertainty about the number of deaths in the events they report.

Here, we propose a simple modelling framework that translates UCDP's expert knowledge into usable uncertainty measures for conflict fatalities. We leverage UCDP’s knowledge through a survey with their coders to construct a model that can generate probability distributions of plausible fatality levels for all possible events and related fatalities coded in the UCDP dataset. We use this model to estimate the probability distribution of the most plausible number of fatalities for any conflict event reported by UCDP. As the survey with UCDP coders gives us expert estimates of uncertainty around reported fatalities by violence type and for varying contexts, the resulting estimates of uncertainty can be generalized across countries and settings.

Our analysis suggests that the coders believe that fatalities in UCDP data are generally under-reported. Further, we find that the relative estimated rate of under-reporting decrease non-linearly with the number of fatalities. For low-fatality events, with a reported best estimate of 100 fatalities or less, the estimated rate of under-reporting is around 100 percent for events with one fatality, decreasing to around 30 percent for events with at least 100 fatalities and approaching zero for very high-fatality events, in excess of 23,000 fatalities. At the same time, the associated uncertainty, that is the width of the estimated fatality distribution, increases considerably with the size of the conflict event.

The results contribute to existing research on measurement uncertainty and media bias in conflict events by augmenting the UCDP ‘best’ death estimates with distributions of plausible values of fatalities, as assessed by the UCDP coders themselves, based on their knowledge of the coding procedures and information sources. Our approach yields two useful additions to the UCDP data: a more general and useful estimate of uncertainty than the current ‘low’ and ‘high’ estimates they provide; and a remedy to UCDP’s tendency to publish conservative estimates of fatalities. As UCDP is one of the most widely used conflict event datasets globally, these additions are particularly relevant to conflict scholars.

Our simulation approach is parsimonious, simple, fast to replicate, can be applied to future releases of UCDP data, and offers a practical tool to address measurement bias in conflict research. To facilitate the uptake of our model, we released the R package uncertainUCDP, which can be used to obtain random draws, quantiles, and densities of the plausible number of fatalities for any arbitrary UCDP event.

In our replication analysis, we show how to account for uncertainty in fatalities in empirical studies of conflict, as a simple solution to increase transparency and strengthen the robustness of regression models using UCDP data.

By improving our understanding of uncertainty around conflict deaths in UCDP data, this approach can open new research avenues on the implications of measurement error for the statistical analysis of conflict data. Our practical solution to account for measurement uncertainty can easily be applied in other domains, and thus contribute to the ongoing debate on measurement error in data derived from expert judgments.

Uncertainty in Conflict Data

We can distinguish two main types of uncertainty in conflict fatalities: first, uncertainty around conflict events that are completely unreported in any source of information. This is the so-called ‘fog of war’, or the inherent uncertainty in conflict fatalities related to missing people that are not recorded as dead, or conflict events that are completely undocumented – what we here refer to as ‘unknown and uncertain’ events.

A second type of uncertainty, also referred to as measurement uncertainty or error, concerns conflict events that are in fact documented, but for which the number of related fatalities is not completely certain. This latter type of uncertainty can arise from how coders apply different definitional aspects (see e.g., Otto 2013; Restrepo et al. 2006; Tabeau and Zwierzchowski 2013), for example concerning the count of fatalities, the actors involved in the conflict, the type of violence (one-sided, state-based, or non-state), and the geographic and temporal precision of the event. Uncertainty around the number of fatalities in documented conflicts can also stem from biases inherent in the underlying information sources.

The problem of measurement error is well-known in social science survey research, which has exposed the challenges in dealing with latent variables from expert judgments (Blackwell et al. 2017; Knutsen et al. 2024; Little and Meng 2024; Marquardt 2020). Although these studies have improved the measurements of concepts like democracy, human rights, or public opinion, the issue of how to account for measurement error into empirical analyses remains unresolved. Standard approaches to handle measurement error are not robust to violations of the assumption of independence (Blackwell et al. 2017): If measurement error is correlated with other variables, the most common approach is to ignore it, but this is equivalent to assume that the data are measured perfectly (Blackwell et al. 2017) and thus distorts inference (e.g., Crabtree and Fariss 2015).

In the peace and conflict domain, studies have mostly focused on the first type of uncertainty referred to above. These studies aim at uncovering the magnitude of completely unreported conflict episodes, which remain undocumented in event datasets coded from the media. This research largely relies on comparisons between media-based event datasets and ‘ground truth’ data collected from non-media sources (Ball and Price 2018; Guzmán et al. 2012; Hoover Green and Ball 2019; Krüger and Ball 2014; Price et al. 2015; Sloboda et al. 2013; Tabeau and Zwierzchowski 2013).

A second strand of literature focuses on detecting contextual factors that shape uncertainties in conflict fatalities, for example by relating media coverage to country or local attributes to uncover parallel trends, or by exploiting information available from the coding process of the event datasets to draw inference about events precision and uncertainty.

These studies find that the precision of conflict reporting varies by country and year in which the event takes place (Davenport and Ball 2002; Herkenrath and Knoll 2011), and is influenced by socio-economic and political conditions including international trade (Dietrich and Eck 2020), freedom of the press, political regime, and presence of human right organizations (Baum and Zhukov 2015; Dietrich and Eck 2020; Drakos and Gofas 2006). Studies also show that different types of violence tend to be reported differently across information sources (Davenport and Ball 2002), depending on their proximity to the event (Gritten et al. 2012), idiosyncratic biases towards violent perpetrators (Ariyanto et al. 2008; Radford et al. 2023) and victims (Otto 2013), as well as political regime type. While media in democracies tend to over-report terrorist episodes as well as non-violent collective actions especially by regime opponents, media outlets in autocracies under-report these events but inflate violent acts by rebel groups (Baum and Zhukov 2015; Drakos and Gofas 2006). Geographic factors such as distance to internet access, information and communication technology, or major settlements also help predict the precision of conflict event reports (Croicu and Kreutz 2017; Dietrich and Eck 2020; Weidmann 2015).

These studies have greatly advanced our knowledge of reporting bias and measurement uncertainty in conflict event datasets. However, they are either concerned with estimating the true, undocumented number of fatalities related to the ‘unknown and uncertain’ events for specific case studies, or they focus on contextual drivers of uncertainty in fatalities related to documented events. A systematic effort to provide a measure of uncertainty in fatalities for events that are documented, and which can be generalized across different contexts, has yet to be undertaken.

Here, we fill this gap by augmenting UCDP estimates of deaths with a probability distribution of plausible values of fatalities, for any fatality level recorded in the UCDP dataset. The resulting distributions provide a more realistic and robust confidence interval around UCDP best estimates than their documented high and low estimates. We leverage the expert knowledge of the UCDP team coupled with statistical simulations to obtain an estimate of uncertainty that is generalizable across information contexts, settings, and violence types.

Methods

UCDP Coding of Fatalities

UCDP defines armed conflict as a violent event where the use of armed force leads to at least 25 battle-related deaths at the country level in one calendar year (Davies et al. 2025; Gleditsch et al. 2002). UCDP consistently reports on three types of organized violence: state-based conflict, where the use of armed force involves at least one governmental actor; non-state conflict between two organized groups, none of which is the government of a state, and one-sided violence, where an organized actor deliberately targets civilians.

For each event and each type of violence, UCDP reports a ‘best estimate’ of the number of people killed, which represents the most reliable count of deaths associated to that event (Högbladh 2025; Sundberg and Melander 2013). We will refer to this as the ‘reported value’ for the event, denoted as ${\tilde{y}}_{i}$ , where i is an index for the specific event. In addition to the reported value, each event record includes a ‘low’ and ‘high’ estimates of fatalities. The UCDP is admittedly conservative in their reporting (e.g., Uppsala Conflict Data Program 2023), and it is plausible that the true number of fatalities is closer to their high estimate than the best estimate, depending on the context of an event and the country in which it occurs.

Importantly, UCDP low and high estimates do not represent a confidence interval around the best estimate: they are the result of a systematic coding process, and reflect the uncertainty and reliability of available sources, but they are not an estimate of measurement error.¹

UCDP distinguishes two types of conflict events by ‘clarity’ (Högbladh, 2025): ‘ordinary’ single events with high clarity (1), and ‘summary’ events with lower clarity (2). Events coded with clarity 1 are those for which the source is sufficiently detailed to identify individual incidents in a single location and date. ‘Summary’ events describe cases when one or multiple sources aggregate violent incidents for a given location over a longer period (e.g., a month), such that individual events cannot be disentangled (Croicu and Sundberg 2013). To avoid double-counting, fatalities from individual events are subtracted from fatalities reported in summary events in the same location and time span. Additional coding rules adopted by UCDP specify how to attribute fatality numbers to ‘high’, ‘low’, and ‘best’ to reflect the uncertainty about conflict contexts and the reliability of sources. For example, when the coding of an event is drawn from a single biased source, UCDP only codes fatalities related to that event as high. In these cases, the high estimate may be lower than the best estimate if later summary events are coded from non-biased sources, as UCDP reports fatalities from such non-biased sources in the best and low category, but subtracts them from the high category to avoid duplication.²

These protocols are fundamental in ensuring consistency in the definition of political violence and comparability across contexts, but they also contribute to explain the variation in reported fatality figures across different conflict event datasets (Öberg and Yilmaz 2025). To increase transparency, UCDP documents these coding rules internally, as well as externally through publicly available codebooks (see Högbladh 2025, https://ucdp.uu.se/downloads/ged/ged251.pdf). However, this information is conveyed in a form that is specific to distinct sub-cases, and is not a practically useful or accessible representation of uncertainty. The UCDP coding protocol also contains a number of other practices based on an understanding of uncertainty which is documented, yet not easily accessible by users. For instance, a news report stipulating ‘many fatalities’ will be coded as 3 fatalities by the UCDP according to their practice. All UCDP coders know in this case that a coding of ‘3 fatalities’ in many cases means ‘3 or more’ rather than ‘3’. A coding of ‘4 fatalities’, on the other hand, is more precise according to this coding convention. These rules are encompassed in the so-called ‘vague number translator’ reported in the online appendix (section A1).

The approach we describe below taps into the UCDP knowledge about all of these documented but internal coding practices, in addition to their accumulated experience and understanding about uncertainty in conflict reporting. We seek to present the UCDP coders’ uncertainty in the form of probability distributions over all possible fatality counts, given the published report. This format has the advantage of being uniform across all cases, but ignores the detailed information about the sources of the uncertainty in the original UCDP coding.

Survey With UCDP Coders

We rely on a survey with UCDP to extract coders’ probabilistic assessments of plausible fatalities – the ‘coder distributions’ – given the reported value and the type of violence recorded in the UCDP dataset.

To this end, we first conducted a workshop with 15 UCDP coders (almost the entire team) to discuss the main concepts, delimit the scope and ambition of the survey, and pilot it. The UCDP coders undergo extensive training on the coding rules and attend weekly consultations on difficult cases, which increases inter-coder reliability. In our sample, coders have 2–5 years of experience, although considerable variations exist, with 3 coders having more than 10 years of experience, and 2 coders having less than 1 year of experience. Variations across coders’ backgrounds, behaviors, and systematic biases in their perception can play a role in our analysis (Marquardt 2020); While we leave it to future research to measure how inter-coder reliability affect uncertainty in conflict data, the approach illustrated here at least partially reflects inter-coder variations in the estimate of uncertainty (see appendix Figures A6–A7).

The workshop focused on UCDP’s coding procedures to understand how uncertainty around the most plausible number of deaths varies depending on the information context, the type of violence, and other characteristics of the country in which the event takes place. The discussion provided us with a broad understanding of how uncertainty is assessed by coders and reflected in their coding rules.

Next, we administered a questionnaire through Qualtrics to elicit probability intervals of ‘actual’ conflict fatalities given the reported ones, and to cover all types of conflict events coded by UCDP. The first part included country-level questions. We first asked respondents to indicate which of the countries they have ever coded for has the ‘best’ information coverage and reliability, and which country has the ‘worst’. The best country represents a context where information sources on the conflict event are relatively accessible, abundant and reliable; the worst, one where information is scarce, incomplete, or unreliable.

We also asked some calibration questions to understand which factors, for example type of violence, information provider, or political regime, shape the availability and reliability of information. As explicitly modeling how each factor shapes uncertainty in fatalities is beyond the scope of this study, these questions are descriptive in nature, and do not link each factor to different levels of fatalities (which would be required for inclusion in the statistical modelling described below). Yet, responses show that coders are highly aware of drivers of uncertainty, in line with the existing literature (see section A1.2 in the appendix for a discussion). This awareness is implicitly accounted for in the coders’ responses, as event-level questions are iterated across information contexts of different quality, letting coders free to adjust their responses based on differences in the information environment.

The main part of the questionnaire included event-level questions, where we retrieved the ‘coder distributions’ – a representation of each coder’s subjective probability density function (PDF) – over the ‘true’ number of fatalities for an event, given how UCDP coded it. We iterated these questions over best and worst information contexts, and the three types of violence coded by UCDP. For a selected number of fatalities reported in the UCDP dataset, which spanned the major contexts identified in preliminary discussions with UCDP (equal to 0, 1, 2, 3, 13, 20, 24, 40, 47, 100, 101, 200, 201, 1,000, 1,001, 2,000, 2,001, 10,000, 100,000) we asked coders how likely it is that the ‘true’ number of fatalities related to the event lies in a specified interval. The response intervals in the survey varied according to the level of reported fatalities for which we asked questions. The response options were: Extremely likely (90–100 percent), Somewhat likely (60–90 percent), Neither likely nor unlikely (40–60 percent), Somewhat unlikely (10–40 percent), Extremely unlikely (0–10 percent). An example survey question and resulting coder distribution is presented in Figure 1, for an event recorded by the UCDP to involve $\tilde{y} = 13$ fatalities.

Figure 1.

Example question, response options and resulting responses in the survey administered to the UCDP team, for ${\tilde{y}}_{i} = 13$ .

We clarified to the coders that the response options did not have to sum to 100, as we would normalize them, and instructed them to complete the questionnaire alone, as variation across their responses was valuable. We collected responses from 13 UCDP coders in May–June 2023. These responses represent binned probabilities of the plausible values of fatalities, given the fatality level that was surveyed, and for different information contexts and types of violence. A sample coder distribution obtained from the survey is provided in Table 1.

Table 1.

Sample coder distribution from individual coder j for the example in Figure 1: UCDP reports $\tilde{y_{i}} = 13$ fatalities, good information context, state-based violence

Bin	Fatality range	Bin probability
1	0	0.01
2	1–3	0.02
3	4–12	0.12
4	13	0.64
5	14–24	0.19
6	25–50	0.015
7	$>$ 50	0.01

The statistical analysis of the survey results is described in the next section.

Statistical Modelling

Our estimation strategy aims to build a simple and generalizable model of uncertainty in UCDP fatalities which can be applied to new releases of UCDP data, while accounting for how uncertainty varies with UCDP coding rules, violence types, and information contexts. As the survey asked a finite number of questions, however, we could collect the coders’ probabilistic assessments only for a limited number of reported values. The task is therefore to construct a model that generalizes these responses beyond the surveyed values. For example, if UCDP codes ${\tilde{y}}_{i} = 37$ fatalities in state-based conflict, we want to use our model to estimate the probability distribution of the most plausible number of fatalities for this event, even though this particular reported value was not included in the survey.

Estimation Strategy: A Model of y_i

We can think of the distribution of plausible values for the true number of fatalities of an event i as a PDF with parameters θ _i, where θ _i in turn is a function of the reported value ${\tilde{y}}_{i}$ and some covariates z _i. The quantity of interest in this study can be then defined as:

y_{i} \sim F (θ_{i} | {\tilde{y}}_{i}, z_{i})

(1)

with:

θ_{i, k} = f (β_{k}, {\tilde{y}}_{i}, z_{i}, ϵ)

(2)

where i indexes a record of a UCDP violent event; y_i is the PDF for the plausible value of the true (unobserved) number of fatalities for i, and θ_k,i is the k:th parameter of the PDF of violent event i. θ_k,i is modelled as a function of

{\tilde{y}}_{i}

, the number of reported fatalities in UCDP for event i, and z _i, a set of variables that capture the uncertainty in information sources and how UCDP translate this uncertainty into a number of fatalities, as well as the information context (best vs worst).³ Specifically, z _i includes (1) a set of dummy variables for reported counts that the UCDP coding processes treat as special values D_i;⁴ (2) dummies for the type of violence; and (3) a binary variable c_i measuring the quality of the information context (best vs worst) according to the UCDP coders. Lastly, ϵ is an error term.

The c_i variable was aggregated directly from coders’ ranking of best and worst information contexts out of all countries they had documented. Coders are aware of how the information quality and quantity vary, and what shapes these variations (see section A1.2), and they incorporate this awareness in their survey responses, as event-level questions are iterated for different information contexts. Therefore, the model implicitly accounts for UCDP’s expert opinion of how uncertainty in conflict reporting varies depending on the coverage and quality of information sources.

Our objective is to identify a parametric distribution with parameters θ _i that best mimics the binned probability distributions assigned by coders to each event (i.e., the coders’ distributions), and that can be generalized to predict ${\hat{θ}}_{i}$ for any UCDP-reported fatality count given a set of covariates.

To achieve this, we implement a two-stage procedure. In the first stage, we consider a range of candidate distributions and, for each distribution, coder j, and event i, estimate the parameter vector ${\tilde{θ}}_{i, k}$ that most closely matches the coder distribution. In the second stage, we model these optimized parameters as functions of observed covariates to obtain event-level predictions for ${\hat{θ}}_{i}$ .

As plausible distributions for fatalities in the first stage, we evaluate the normal, lognormal, Gumbel, negative binomial, and Poisson distributions, as well as reported-value inflated (RVI) mixture variants of each. The RVI mixture distributions combine a standard parametric distribution with a point distribution at the reported UCDP value, governed by an additional parameter, the mixture weight w. The RVI mixture distributions are designed to capture coders’ tendency to place disproportionate probability on the recorded estimate relative to what the standard distribution would imply.

In the first stage of the procedure, we obtain optimal parameters ${\tilde{θ}}_{i, k}$ using a genetic algorithm that searches over a reasonable parameter space. Candidate distributions and parameter sets are evaluated using the Binned Absolute Difference (BAD), defined as the sum of absolute differences between the coder distribution and the parametric distribution discretized to the same bins. BAD ranges from 0 (identical distributions) to 2 (non-overlapping distributions). The algorithm is run for 100 generations, which reliably yields near-optimal solutions.⁵

In the second stage, we estimate regression models to predict the ${\tilde{θ}}_{i, k}$ optimized in the first stage, as a function of the UCDP reported value of fatalities ${\tilde{y}}_{i}$ , and a set of variables proxying the information context, z _i. Model performance is evaluated using a leave-one-coder-out cross-validation: For each event, we predict ${\hat{θ}}_{i}$ while holding out one coder and compute the resulting BAD score. Final performance is summarized using the median BAD across iterations, which is robust to extreme values. The full procedure is illustrated in Figure 2.

Figure 2.

The two-step procedure for evaluating any given distribution, presented for two example coders and two fatality levels and for the point-value inflated lognormal distribution.

Our preferred model, presented in the next section, includes the combination of covariates that best approximates the coders’ distribution, while also retaining parsimoniousness and generalizability.

Results

Model Specifications That Best Approximate Coders’ Distribution of Uncertainty

The mean and median BAD scores for the best performing specification per distribution and type of violence are shown in Tables A2–A5 in the Online Appendix. As all types of violence show similar aggregate coders’ distributions (Figures A1–A4), we focus on state-based violence in the remainder of the paper. While differences across distributions are modest, the results consistently indicate that the RVI Gumbel mixture yields the lowest aggregate median BAD scores across violence types.⁶

The RVI Gumbel mixture distribution is characterized by three parameters: a location parameter θ₁ = μ_g, which determines the center of the continuous component; a scale parameter θ₂ = β_g, which governs the dispersion of plausible fatality counts; and a mixture weight θ₃ = w, which represents the excess probability mass placed on the reported UCDP fatality count (i.e., inflation at the reported value). The strong performance of the RVI Gumbel mixture distribution is in line with expectations about uncertainty in fatalities: its right-skewed shape accommodates the asymmetric nature of reporting uncertainty; its lighter upper tail relative to the log-normal distribution avoids assigning undue weight to extreme values that coders view as unlikely, while the RVI – which concentrates probability at the best estimate – captures the coders’ belief that the UCDP reported value of fatalities is the single most likely value, even when acknowledging substantial uncertainty.⁷ This explains why mixture distributions consistently outperform their non-mixture counterparts.

In the second stage, where we predict $\hat{θ}$ from $\tilde{θ}$ , we test a full battery of models to predict the three parameters of the gumbel mixture distribution, which best approximates coders’ believes about uncertainty in fatalities. The models featured different combinations of covariates, including the UCDP-reported value $\tilde{y}$ (logged), dummy variables D_i for UCDP special ‘vague numbers’, and a binary variable c_i for the information context, reflecting coders’ knowledge of how information availability and quality vary. As c_i implicitly captures other contextual factors that shape uncertainty in conflict reporting, we expected it to carry considerable influence on the models’ performance.

The results of our tests are shown in Tables A6–A9 in the Online Appendix, which report the mean and median BAD scores for the different types of violence and for different sets of covariates. Contrary to our expectations, including c_i does not substantially improve accuracy: For both the coders’ distributions (Figures A1–A3) and the modelled distribution of fatalities (Figure A4)⁸ differences in predictive performance when including or omitting the information context c_i are marginal. Uncertainty is predominantly explained by the intensity of violence reported in the UCDP dataset, as well as internal coding rules defining how textual information is translated into numbers (proxied by D_i).

This is an important insight: Internal coding rules have largely been neglected by research on measurement error in conflict data, which has mostly focused on the role of external factors in driving reporting accuracy. This omission is striking given the substantial variation in coding rules across event datasets. Understanding how internal coding processes shape uncertainty is particularly urgent given the rapid adoption of Large Language Models for automated conflict event data coding (Brandt and Sianan 2025; Skorupa Parolin et al. 2022; Steinert and Kazenwadel 2025), as algorithms trained on human-coded data may reproduce and amplify dataset-specific biases into systematic, large-scale distortions.

Given the minor contribution of the c_i term to predictive performance, and since omitting it from the model saves us from estimating the quality of information context for any individual event, we opt for a parsimonious specification that includes $\tilde{y}$ (logged) and the special dummies D_i for all three parameters of the distribution and for all levels of violence. Different combinations of these covariates feature among the best performing specifications across all types of violence.

These covariates explicitly capture the uncertainty around UCDP coders’ application of the violence definition, their rules on how to translate textual sources into fatality counts, and their beliefs about how uncertainty varies with reported fatality levels. Moreover, the model implicitly reflects coders’ beliefs about how reporting uncertainty varies across contexts, as their survey responses were elicited across diverse countries and information environments. The resulting aggregate distribution, which the model generalizes, incorporates the full range of coders’ assessments, informed by their extensive experience on how contextual factors shape information availability, quality, and potential systematic under- or overreporting across conflicts. A broad set of context- and conflict-specific considerations, extending beyond the vague number translator, are implicitly encompassed in the model. This might explain why adding the binary variable c_i does not improve the model’s performance. Overall, our simple approach can provide a measure of uncertainty in UCDP’s best estimates that generalizes across countries and settings – which is important to ensure consistency with the cross-national and time-varying UCDP data.

The results from the regression analysis (Table A10), show that there is a strong linear relationship between the logarithm of the number of reported fatalities and the location parameter of the gumbel distribution, indicating that higher reported mean values are associated with higher values in the plausible fatality distribution.

Using the predicted parameters, we compute the mean of the RVI gumbel mixture distribution and compare it against the reported value. The results, visualized in Figure 3, suggest that UCDP fatality counts tend to be under-reported on average, with the estimated under-reporting – defined as the ratio between the predicted mean and the reported count – decreasing non-linearly with the number of reported fatalities. For reported fatality levels under 100, the estimated rate of under-reporting ranges from over 100 percent – for events with 1 fatality – to around 30 percent – for events with 100 fatalities. For medium levels of conflict intensity (e.g., 100–500), the ratio of under-reporting continues to decrease, reaching approximately 25 percent for 250 fatalities and 20 percent for 500 fatalilties. At very high levels of violence (e.g., $\tilde{y} > 23,000$ ) the predicted mean of the plausible distribution aligns more closely with the reported value, although a non-negligible rate of under-reporting remains.

Figure 3.

The line shows the predicted μ, denoting the overall mean of the plausible fatality level (y axis) for any given level of fatality reported by UCDP (x axis), according to our preferred model specification. The dots represent the predicted means for each sample of ${\tilde{y}}_{i}$ for which we have coder distributions. The dotted lines represent the 97.5th and 2.5th percentiles of the resulting distribution.

Moreover, the results show that the dummies for the vague numbers in the UCDP generally have negative effects on the first two parameters of the distribution, indicating that, according to the coders, these vague numbers (except for $\tilde{y} = 2$ ) are less prone to be under-reported than other numbers. This reflects coders’ awareness that reports of “hundreds” of fatalities often represent upward-rounded estimates from media sources, in contrast to more precise numeric figures.

We can also use the fitted regression models to produce densities of plausible actual values of fatalities given a specific reported value and type of violence, and for any given event in the UCDP dataset. Figure 4 shows the densities of the plausible number of fatalities for eight different $\tilde{y}$ . The figure highlights that the relative estimated rate of under-reporting is higher for lower levels of violence, although with some important non-linear variations. For example, there is a non-negligible risk of over-reporting for the special number $\tilde{y} = 24$ , which translates ‘dozens’ of fatalities, and represents UCDP’s yearly cutoff for defining an event as an active armed conflict. This is particularly evident when comparing the distribution for this special value to the distribution of 32 fatalities. For around 23,000 reported fatalities, the mean and median of the distribution are close to the reported value. For even higher number of fatalities, the mean and median of the predicted distribution are lower than $\tilde{y}$ , although there remains a non-negligible risk of under-reporting, with a 10 percent likelihood that the number of fatalities is under-reported by at least 4,000.⁹

Figure 4.

The facets each show a density for a specific $\tilde{y}$ , the black dashed line correspond to $\tilde{y}$ and the colored dashed lines correspond to the median, mean, and 90th percentile of the distribution.

To facilitate the uptake of this model, we released the R package uncertainUCDP that enables generating a distribution of plausible fatalities for any UCDP event, conditional on the reported fatality count $\tilde{y}$ and type of violence, using the regression models presented here. The resulting distribution can be used to extract quantities like means, quantiles, densities, or random draws.

Distributions of Fatalities Across Aggregated Events

The framework outlined above allows us to move from point estimates of reported fatalities to full predictive distributions for individual UCDP events, conditional on the reported count $\tilde{y_{i}}$ . We illustrate the practical implications of this approach by simulating plausible distributions of total fatalities in Ethiopia and Syria during 2020–2022. These cases are chosen to contrast a context with relatively good information (Syria) with a more uncertain information environment (Ethiopia).

Specifically, we predict the distribution of plausible fatality level for all state-base conflict events reported by UCDP in each country in 2020–2022. Next, we make a random draw from the predicted distribution for each event and sum the total number of fatalities across all events. We repeat the procedure 1,000 times to obtain a distribution of the total plausible number of fatalities for each case.

Figure 5 shows the predicted distribution of the total number of fatalities in 2020–2022 for the two cases. The figure shows that for Syria (2020–2022), the mean of the plausible distribution of the actual fatalities is almost 60 percent higher than the actual number reported in the UCDP dataset. In a good-information case like this, our procedure effectively counters the conservative bias, while showing that the uncertainty with respect to total number of fatalities is relatively small – in 90 percent of simulations the total fatalities are between 11,193 and 11,950. Even if the UCDP coders think that there is considerable uncertainty within each report (see Figure 4), pooling the large number of independent reports present in a good-information context like Syria yields a moderate overall estimate of uncertainty.

Figure 5.

Distribution of plausible fatalities values for Syria (left) and Ethiopia (right) in 2020–2022, aggregated across all violent events. Black dashed line shows sum of the UCDP’s ‘best’ estimates, colored dashed lines the median and mean, as well as the 5th and 95th percentile of the estimated distribution.

For a poor-information context like Ethiopia, our results are quite different. First, the resulting uncertainty estimate for fatalities in reported events is much wider relative to the central value – 90 percent of the simulations fall in an interval ranging from 98,187 to 125,417. The central tendency of this distribution, however, is much closer to what the UCDP has reported as the best estimates. A primary reason for this is that UCDP coders generally regard fatality estimates in large events as less likely to be under-reported than for small events.¹⁰ Such high-fatality events are typically reported as aggregated “summary” events by reputable sources. In line with our previous results, such events are characterized by a higher degree of uncertainty (i.e., wider distribution) but a lower rate of under-reporting. This means that pooling a larger number of small events tend to yield less uncertain fatality distributions, but with a higher rate of estimated under-reporting. Conversely, pooling a smaller number of large events tend to yield more uncertain fatality distribution but a lower rate of under-reporting. This is in line with the coders’ intuition that high-fatality summary events are more uncertain, but less likely to be under-reported.

These results highlight that our estimate of uncertainty varies non-linearly with the number of reported fatalities, confirming that the model represents uncertainty well beyond the vague numbers in UCDP coding rules. These non-linear variations have important implications for measurement bias in conflict research, as they add nuance to the standard assumption that larger conflict events are automatically more likely to be reported, and may thus complement recommended empirical tests for detecting measurement bias which lie on this assumption (e.g., Weidmann 2016).

Replication Study

Our study provides a practical tool to address measurement uncertainty and biases in conflict event data and to test its potential implications in empirical analyses of conflict. We illustrate how integrating uncertainty around UCDP fatalities data can affect empirical findings by replicating the analysis of Brosché and Sundberg (2024). Our illustrative replication exercise exemplifies how to incorporate uncertainty estimates around conflict fatalities by replacing the best estimates reported by UCDP with random draws from our modelled distribution of plausible fatalities.

As our uncertainty model is calibrated to fatalities related to UCDP-GED events, we limit the replication dataset to dyad-years which are available in the UCDP-GED dataset, and replace the number of fatalities in the original dataset with dyad-year aggregates drawn from UCDP-GED version 23.1 (Davies et al. 2023). We integrate our uncertainty estimates as follows: in each simulation, we replace the fatalities in the replication dataset with a random draw of fatalities for each related UCDP event from our model. We then aggregate the simulated fatalities to the dyad-year level, update and lag the dependent variable, and run the same models as Brosché and Sundberg (2024) – a model with country-level fixed effects and a pooled model with standard errors clustered at the country level. We repeat this process 100 times and calculate the coefficients and standard errors using the Rubin rules. The Rubin rules were originally developed for combining multiple imputation datasets (Rubin 1987); the same logic can, however, be used to combine the results from multiple simulations (Croicu 2024).

This approach is quite simple, and aims to showcase how uncertainty estimates can be integrated in empirical research. However, relying on two-stages method of composition approaches, as the one used here, can perform poorly in settings with strong endogeneity between measurement and outcome processes, where unified Bayesian approaches estimating these processes jointly are preferable (Marquardt 2020).

The results are presented in Table 2. The first and third columns shows the coefficients and standard errors from the models using the original dataset; the second and fourth columns show the results from the models using the simulated fatalities. As the Table shows, coefficients are similar in both models, but the standard errors for the battle-related deaths coefficients are approximately 29–56 percent larger in the models using simulated fatalities. Standard errors for the number of issues (the main independent variable in the original study) are approximately 15–29 percent larger when replacing the original best estimates with our simulated fatality values. This increase in standard error is considerable, and, although it does not affect statistical significance at the conventional level in this case, it may affect conclusions in other studies, especially if treatment effects are weaker or sample sizes smaller.

Table 2.

Replication study of Brosché and Sundberg (2024) using simulated fatalities (columns 2, 4) in place of the original best estimates (columns 1, 3). Standard errors are in parentheses.

	Model 1	Model 1	Model 2	Model 2
	(Original, FE)	(Uncertainty, FE)	(Original, clustered SE)	(Uncertainty, clustered SE)
Battle deaths (log, t-1)	0.622***	0.605***	0.712***	0.696***
Battle deaths (log, t-1)	(0.023)	(0.036)	(0.041)	(0.053)
N. of issues (log)	0.260***	0.263**	0.191+	0.198
N. of issues (log)	(0.065)	(0.084)	(0.110)	(0.127)
External support	0.269**	0.314**	0.334*	0.353*
External support	(0.094)	(0.122)	(0.131)	(0.144)
Discriminated population	−0.156	−0.096	−0.514	−0.589
Discriminated population	(0.451)	(0.668)	(0.493)	(0.546)
Excluded population	0.398	0.324	0.106	0.132
Excluded population	(0.354)	(0.436)	(0.518)	(0.560)
Electoral democracy index	0.178	0.222	−0.812	−0.776
Electoral democracy index	(0.433)	(0.525)	(0.498)	(0.522)
Population (log)	−0.998***	−0.930**	−0.110+	−0.097
Population (log)	(0.261)	(0.352)	(0.063)	(0.066)
GDP per capita (log)	0.304*	0.301+	0.040	0.043
GDP per capita (log)	(0.119)	(0.158)	(0.068)	(0.075)
Num.Obs.	1,014	1,014	1,021	1,021

Note. +p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001.

We also compare the predicted values of the models using the original replication dataset versus the simulated fatalities. The predicted values for different number of conflict issues (on a log scale) are presented in Figure 6. Notably, the predicted values are substantially higher for the models using simulated fatalities, reflecting the general trend towards under-reporting fatalities we observed in our analysis.

Figure 6.

Battle related deaths (log) predicted by our uncertainty model (green, orange), versus the original estimates from Brosché and Sundberg (2024) (red, blue).

The process illustrated here can be applied and refined by future studies of conflict to test the sensitivity of their findings to measurement error. Explicitly accounting for and reporting uncertainty in empirical results can increase transparency and robustness, and thus contribute to tackle the reproducibility crisis in social sciences (see Simkus et al. 2025, for a review).

Conclusions

In this paper, we have estimated uncertainty around the number of fatalities reported in the UCDP dataset for different types of violence and information contexts, combining a survey with UCDP coders with a simulation approach. We find that reported fatalities in the UCDP dataset generally suffer from under-reporting, especially at very low levels of fatalities, for which there tends to be a relatively higher conservativity bias than for events with higher levels of fatalities. However, uncertainty (the width of the plausible fatalities distribution) is higher for high-intensity conflict events.

The estimated probability distributions can be used as confidence intervals around the best estimate provided by UCDP, avoiding the misuse of low and high estimates as uncertainty boundaries. Our approach to model uncertainty in conflict data, easily implemented through our uncertainUCDP R package, offers a pragmatic solution to the problem of measurement uncertainty that can be generalized to other domains. In turn, accounting for measurement uncertainty in conflict data can strengthen the soundness and transparency of empirical studies.

This analysis has important limitations. First, our uncertainty estimates cannot capture fatalities from events that are entirely unreported, since UCDP experts cannot observe events that never reach any source. Our estimates concern only the distribution of documented deaths, which cannot be validated against a ‘ground truth’ – as this truth is not available across countries and contexts. Another limitation of this study is inherent in its reliance on UCDP coders: Although our approach to some extent reflects variations across coders, we do not measure how and to what extent our estimates are driven by coders’ individual characteristics. In line with the literature on expert reliability in surveys (Bakker et al. 2014; Lindstädt et al. 2020; Marquardt et al. 2019), an avenue for future research is to assess how coding of conflict events vary as a function of these individual characteristics. Further, our approach reflects UCDP coders’ expert knowledge of variations in information contexts, but does not systematically explore how different factors shape the quality and quantity of available information, and how this affects reporting uncertainty. Future research can refine the model presented here to explicitly incorporate these contextual factors. Lastly, these results cannot be directly generalized to other conflict event datasets, as our approach was specifically designed for UCDP data and coders. However, our modelling framework could easily be replicated in other domains, facilitating cross-datasets validation practices, and shedding new light on the broader debate around measurement error in expert-derived estimates.

Supplemental Material

Supplemental Material - The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders

Supplemental Material for The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders by Paola Vesco, David Randahl, Håvard Hegre, Stina Högbladh, and Mert Can Yilmaz in Journal of Conflict Resolution

Supplemental Material

Supplemental Material - The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders

Footnotes

Acknowledgements

We thank the UCDP team for contributing their vast experience through their workshop participation and questionnaire responses. We are grateful to two anonymous reviewers for their constructive feedback that greatly improved the manuscript, and to participants of the American Political Science Association Annual Convention in 2023, the workshop at the Center for Advanced Studies held in Oslo in March 2023, the 22nd Jan Tinbergen Conference, the PaCE team workshop in Dublin in 2024, and the 2024 DEMSCORE conference in Gothenburg for their useful comments. For more information on the UCDP and VIEWS and UCDP projects see https://ucdp.uu.se and .

ORCID iDs

Paola Vesco

David Randahl

Håvard Hegre

Stina Högbladh

Mert Can Yilmaz

Author Contributions

PV: Conceptualization, investigation, methodology, supervision, project administration, data curation, writing – original draft preparation, writing – review and editing, validation. DR: Data curation, software, formal analysis, methodology, investigation, visualization, writing – original draft preparation, writing – review and editing. HH: Conceptualization, methodology, funding acquisition, writing – original draft preparation, writing – review and editing. SH: Validation, writing – review and editing. MYC: Validation, writing – review and editing.

Supplemental Material

Supplemental material for this article is available online.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded by Riksbankens Jubileumsfond through the programme Societies at Risk, Uppsala University, the European Research Council under Horizon Europe (Grant agreement No. 101055176, ANTICIPATE), the Research Council of Norway (Grant agreement n. 334977, UFFAC), and the Center for Advanced Study (CAS) at The Norwegian Academy of Science and Letters.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data have been made available in the form of an R package uncertainUCDP (https://cran.r-project.org/web/packages/uncertainUCDP/index.html) producing the dataset from the UCDP API – also see https://github.com/Doktorandahl/the_underreported_death_toll_of_wars. The data are also available for download from viewsforecasting.org as well as at the Harvard dataverse (https://doi.org/10.7910/DVN/INK4KT) ().

Notes

References

Ariyanto

Hornsey

M. J.

Morton

T. A.

Gallois

. 2008. “Media Bias During Extreme Intergroup Conflict: the Naming Bias in Reports of Religious Violence in Indonesia.” Asian Journal of Communication 18 (1): 16–31.

Bakker

Jolly

Polk

Poole

. 2014. “The European Common Space: Extending the Use of Anchoring Vignettes.” The Journal of Politics 76 (4): 1089–1101.

Ball

Price

. 2018. “The Statistics of Genocide.” Chance 31 (1): 38–45.

Baum

M. A.

Zhukov

Y. M.

. 2015. “Filtering Revolution: Reporting Bias in International Newspaper Coverage of the Libyan Civil War.” Journal of Peace Research 52 (3): 384.

Blackwell

Honaker

King

. 2017. “A Unified Approach to Measurement Error and Missing Data: Details and Extensions.” Sociological Methods & Research 46 (3): 342–69.

Brandt

P. T.

Sianan

. 2025. “Measurement of Event Data from Text.” Frontiers in Political Science 6: 1453640.

Brosché

Sundberg

. 2024. “What They Are Fighting for - Introducing the UCDP Conflict Issues Dataset.” Journal of Conflict Resolution 68 (10): 2128–2157.

Bury

K. V.

1999. Statistical Distributions in Engineering. Cambridge University Press.

Crabtree

C. D.

Fariss

C. J.

. 2015. “Uncovering Patterns Among Latent Variables: Human Rights and de Facto Judicial Independence.” Research & Politics 2 (3): 2053168015605343.

10.

Croicu

2024. “Deep Active Learning for Data Mining From Conflict Text Corpora.” (visited on 07/04/2025). Pre-published. https://arxiv.org/abs/2402.01577

11.

Croicu

Kreutz

. 2017. “Communication Technology and Reports on Political Violence: Cross-National Evidence Using African Events Data.” Political Research Quarterly 70 (1): 19–31.

12.

Croicu

Sundberg

. 2013. “UCDP Georeferenced Event Dataset Codebook Version 4.0.” https://www.pcr.uu.se/research/ucdp/datasets/ucdp\_ged/

13.

Davenport

Ball

. 2002. “Views to a Kill: Exploring the Implications of Source Selection in the Case of Guatemalan State Terror, 1977-1995.” Journal of Conflict Resolution 46 (3): 427–450.

14.

Davies

Pettersson

Öberg

. 2023. “Organized Violence 1989-2022, and the Return of Conflict Between States.” Journal of Peace Research 60 (4): 691–708.

15.

Davies

Pettersson

Sollenberg

Öberg

. 2025. “Organized Violence 1989–2024, and the Challenges of Identifying Civilian Victims.” Journal of Peace Research 62 (4): 1223–1240.

16.

Dietrich

Eck

. 2020. “Known Unknowns: Media Bias in the Reporting of Political Violence.” International Interactions 46 (6): 1043–1060.

17.

Drakos

Gofas

. 2006. “The Devil You Know But Are Afraid to Face: Underreporting Bias and its Distorting Effects on the Study of Terrorism.” Journal of Conflict Resolution 50 (5): 714–735.

18.

Embrechts

Klüppelberg

Mikosch

. 1997. Modelling Extremal Events: For Insurance and Finance. Springer Verlag.

19.

Gleditsch

N. P.

Wallensteen

Eriksson

Sollenberg

Strand

. 2002. “Armed Conflict 1946–2001: A New Dataset.” Journal of Peace Research 39 (5): 615–637.

20.

Gritten

Mola-Yudego

Delgado-Matas

. 2012. “Media Coverage of Forest Conflicts: A Reflection of the Conflicts’ Intensity and Impact?” Scandinavian Journal of Forest Research 27 (2): 143–153.

21.

Guzmán

Guberek

Price

. 2012. Unobserved Union Violence: Statistical Estimates of the Total Number of Trade Unionists Killed in Colombia, 1999-2008. Tech. Rep. Benetech.

22.

Herkenrath

Knoll

. 2011. “Protest Events in International Press Coverage: An Empirical Critique of Cross-National Conflict Databases.” International Journal of Comparative Sociology 52 (3): 163–180.

23.

Högbladh

2025. “UCDP GED Codebook Version 25.1.” (visited on 01/26/2025). https://ucdp.uu.se/downloads/ged/ged251.pdf

24.

Hoover Green

Ball

. 2019. “Civilian Killings and Disappearances During Civil War in El Salvador (1980-1992).” Demographic Research 41: 781–814.

25.

Knutsen

C. H.

Marquardt

K. L.

Seim

, et al. 2024. “Conceptual and Measurement Issues in Assessing Democratic Backsliding.” PS: Political Science & Politics 57 (2): 162–177.

26.

Krüger

Ball

. 2014. Evaluation of the Database of the Kosovo Memory Book. Tech. rep. Human Rights Data Analysis Group.

27.

Lindstädt

Proksch

S. O.

Slapin

J. B.

. 2020. “When Experts Disagree: Response Aggregation and Its Consequences in Expert Surveys.” Political Science Research and Methods 8 (3): 580–588.

28.

Little

A. T.

Meng

. 2024. “Measuring Democratic Backsliding.” PS: Political Science & Politics 57 (2): 149–161.

29.

Marquardt

K. L.

2020. “How and How Much Does Expert Error Matter? Implications for Quantitative Peace Research.” Journal of Peace Research 57 (6): 692–700.

30.

Marquardt

K. L.

Pemstein

Seim

Wang

Y. t.

. 2019. “What Makes Experts Reliable? Expert Reliability and the Estimation of Latent Traits.” Research & Politics 6 (4): 1–8.

31.

Öberg

Yilmaz

M. C.

. 2025. “Measurement Issues in Conflict Event Data: Addressing Some Misconceptions About What Drives Differences Between Human-Coded Event Datasets.” Research & Politics 12 (3): 1–9.

32.

Otto

2013. “Coding One-Sided Violence From Media Reports.” Cooperation and Conflict 48 (4): 556–566.

33.

Price

Ghodes

Ball

. 2015. “Documents of War: Understanding the Syrian Conflict.” Significance 12: 14–19.

34.

Radford

B. J.

Dai

Stoehr

Schein

Fernandez

Sajid

. 2023. “Estimating Conflict Losses and Reporting Biases.” Proceedings of the National Academy of Sciences of the United States of America 120 (34): e2307372120.

35.

Restrepo

J. A.

Spagat

Vargas

J. F.

. 2006. “Special Data Feature; the Severity of the Colombian Conflict: Cross-Country Datasets Versus New Micro-Data.” Journal of Peace Research 43 (1): 99–115.

36.

Rubin

D. B.

1987. Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Statistics. 1st ed. Wiley.

37.

Simkus

Coolen-Maturi

Coolen

F. P. A.

Bendtsen

. 2025. “Statistical Perspectives on Reproducibility: Definitions and Challenges.” Journal of Statistical Theory and Practice 19 (3): 40.

38.

Skorupa Parolin

Hosseini

, et al. 2022. “Multi-CoPED: A Multilingual Multi-Task Approach for Coding Political Event Data on Conflict and Mediation Domain.” In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. Aies ’22, 700–711. Association for Computing Machinery.

39.

Sloboda

Dardagan

Spagat

Hsiao-Rei Hicks

. 2013. “Iraqi Body Count.” In Counting Civilian Casualties, edited by Seybolt

T. B.

Aronson

J. D.

Fischhoff

, 53–76. Oxford University Press.

40.

Steinert

C. V.

Kazenwadel

. 2025. “How User Language Affects Conflict Fatality Estimates in ChatGPT.” Journal of Peace Research 62 (4): 1128–1143.

41.

Sundberg

Melander

. 2013. “Introducing the UCDP Georeferenced Event Dataset.” Journal of Peace Research 50 (4): 523–532.

42.

Tabeau

Zwierzchowski

. 2013. “A Review of Estimation Methods for Victims of the Bosnian War and the Khmer Rouge Regime.” In Counting Civilian Casualties,edited by Seybolt

T. B.

Aronson

J. D.

Fischhoff

, 213–241. Oxford University Press.

43.

Uppsala Conflict Data Program . 2023. Government of Syria - Civilians. (visited on 08/14/2023). https://ucdp.uu.se/additionalinfo/1178/6

44.

Vesco

Paola

Randahl

David

Hegre

Håvard

Högbladh

Stina

Yilmaz

Mert Can

. 2026. “Replication Data for: The underreported death toll of wars: a probabilistic reassessment from a structured expert elicitation.” doi:10.7910/DVN/INK4KT.

45.

Weidmann

N. B.

2015. “On the Accuracy of Media-Based Conflict Event Data.” Journal of Conflict Resolution 59 (6): 1129–1149.

46.

Weidmann

2016. “A Closer Look at Reporting Bias in Conflict Event Data.” American Journal of Political Science 60 (1): 206–218.

The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders

Abstract

Keywords

Introduction

Uncertainty in Conflict Data

Methods

UCDP Coding of Fatalities

Survey With UCDP Coders

Statistical Modelling

Estimation Strategy: A Model of y i

Results

Model Specifications That Best Approximate Coders’ Distribution of Uncertainty

Distributions of Fatalities Across Aggregated Events

Replication Study

Conclusions

Supplemental Material

Supplemental Material - The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders

Supplemental Material

Supplemental Material - The Underreported Death Toll of Wars: A Probabilistic Reassessment From a Survey with UCDP Coders

Footnotes

Acknowledgements

ORCID iDs

Author Contributions

Supplemental Material

Funding

Declaration of Conflicting Interests

Data Availability Statement

Notes

References

Estimation Strategy: A Model of y_i