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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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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. |
format | Online Article Text |
id | pubmed-10450422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
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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