The underreported death toll of wars: a probabilistic reassessment from a structured expert elicitation

TL;DR

This paper introduces a probabilistic method using expert elicitation and statistical modeling to better estimate the true death toll of wars, addressing reporting biases in existing conflict datasets.

Contribution

It provides a novel, generalizable approach to quantify uncertainty in conflict death estimates, improving accuracy over traditional datasets like UCDP.

Findings

Provides a distribution of plausible conflict fatalities

Offers a dataset and R package for measurement uncertainty

Highlights the under-reporting bias in conflict data

Abstract

Event datasets including those provided by Uppsala Conflict Data Program (UCDP) are based on reports from the media and international organizations, and are likely to suffer from reporting bias. Since the UCDP has strict inclusion criteria, they most likely under-estimate conflict-related deaths, but we do not know by how much. Here, 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, and make available a dataset and R package accounting for the measurement uncertainty. We use a structured expert elicitation combined with statistical modelling 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. The results can help scholars understand the extent of bias affecting their empirical analyses of organized violence and contribute to improve the accuracy of conflict forecasting systems.