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Estimating conflict losses and reporting biases

Determining the number of casualties and fatalities suffered in militarized conflicts is important for conflict measurement, forecasting, and accountability. However, given the nature of conflict, reliable statistics on casualties are rare. Countries or political actors involved in conflicts have in...

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Autores principales: Radford, Benjamin J., Dai, Yaoyao, Stoehr, Niklas, Schein, Aaron, Fernandez, Mya, Sajid, Hanif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450422/
https://www.ncbi.nlm.nih.gov/pubmed/37579154
http://dx.doi.org/10.1073/pnas.2307372120
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author Radford, Benjamin J.
Dai, Yaoyao
Stoehr, Niklas
Schein, Aaron
Fernandez, Mya
Sajid, Hanif
author_facet Radford, Benjamin J.
Dai, Yaoyao
Stoehr, Niklas
Schein, Aaron
Fernandez, Mya
Sajid, Hanif
author_sort Radford, Benjamin J.
collection PubMed
description Determining the number of casualties and fatalities suffered in militarized conflicts is important for conflict measurement, forecasting, and accountability. However, given the nature of conflict, reliable statistics on casualties are rare. Countries or political actors involved in conflicts have incentives to hide or manipulate these numbers, while third parties might not have access to reliable information. For example, in the ongoing militarized conflict between Russia and Ukraine, estimates of the magnitude of losses vary wildly, sometimes across orders of magnitude. In this paper, we offer an approach for measuring casualties and fatalities given multiple reporting sources and, at the same time, accounting for the biases of those sources. We construct a dataset of 4,609 reports of military and civilian losses by both sides. We then develop a statistical model to better estimate losses for both sides given these reports. Our model accounts for different kinds of reporting biases, structural correlations between loss types, and integrates loss reports at different temporal scales. Our daily and cumulative estimates provide evidence that Russia has lost more personnel than has Ukraine and also likely suffers from a higher fatality to casualty ratio. We find that both sides likely overestimate the personnel losses suffered by their opponent and that Russian sources underestimate their own losses of personnel.
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spelling pubmed-104504222023-08-26 Estimating conflict losses and reporting biases Radford, Benjamin J. Dai, Yaoyao Stoehr, Niklas Schein, Aaron Fernandez, Mya Sajid, Hanif Proc Natl Acad Sci U S A Social Sciences Determining the number of casualties and fatalities suffered in militarized conflicts is important for conflict measurement, forecasting, and accountability. However, given the nature of conflict, reliable statistics on casualties are rare. Countries or political actors involved in conflicts have incentives to hide or manipulate these numbers, while third parties might not have access to reliable information. For example, in the ongoing militarized conflict between Russia and Ukraine, estimates of the magnitude of losses vary wildly, sometimes across orders of magnitude. In this paper, we offer an approach for measuring casualties and fatalities given multiple reporting sources and, at the same time, accounting for the biases of those sources. We construct a dataset of 4,609 reports of military and civilian losses by both sides. We then develop a statistical model to better estimate losses for both sides given these reports. Our model accounts for different kinds of reporting biases, structural correlations between loss types, and integrates loss reports at different temporal scales. Our daily and cumulative estimates provide evidence that Russia has lost more personnel than has Ukraine and also likely suffers from a higher fatality to casualty ratio. We find that both sides likely overestimate the personnel losses suffered by their opponent and that Russian sources underestimate their own losses of personnel. National Academy of Sciences 2023-08-14 2023-08-22 /pmc/articles/PMC10450422/ /pubmed/37579154 http://dx.doi.org/10.1073/pnas.2307372120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Social Sciences
Radford, Benjamin J.
Dai, Yaoyao
Stoehr, Niklas
Schein, Aaron
Fernandez, Mya
Sajid, Hanif
Estimating conflict losses and reporting biases
title Estimating conflict losses and reporting biases
title_full Estimating conflict losses and reporting biases
title_fullStr Estimating conflict losses and reporting biases
title_full_unstemmed Estimating conflict losses and reporting biases
title_short Estimating conflict losses and reporting biases
title_sort estimating conflict losses and reporting biases
topic Social Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450422/
https://www.ncbi.nlm.nih.gov/pubmed/37579154
http://dx.doi.org/10.1073/pnas.2307372120
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