Volume 48 - Article 31 | Pages 883–898
Ethnic and regional inequalities in Russian military fatalities in Ukraine: Preliminary findings from crowdsourced data
Abstract
Objective: This paper investigates ethnic and regional disparities in fatality rates in the Russian military in 2022‒2023 during the war in Ukraine.
Methods: The analysis uses a new crowdsourced dataset comprising the names of over 20,000 Russian soldiers killed in Ukraine between February 2022 and April 2023. This dataset was compiled by a team of volunteers who gathered information from social media and other accessible sources. The dataset is incomplete and therefore the findings reported in this paper are tentative. Mortality rates and relative risks are estimated by ethnic group and region, and a linear model is fitted to assess the correlation between the ethnic composition of the population, socioeconomic factors, and regional fatality rates.
Results: The study reveals significant disparities in military fatality rates across Russian regions, with the highest mortality observed among soldiers originating from economically disadvantaged areas in Siberia and the Russian Far East and the lowest among soldiers from Moscow and St. Petersburg. Buryats and Tuvans are overrepresented among the fatalities relative to their population share. However, when regional socioeconomic disparities are accounted for, ethnic differences in mortality rates are considerably reduced.
Conclusions: The observed regional and ethnic fatality disparities appear to be driven by socioeconomic inequalities between regions.
Contribution: This paper evaluates social inequalities in fatalities in the Russian military in Ukraine and compares these findings with research on US military casualties.
Author's Affiliation
- Alexey Bessudnov - University of Exeter, United Kingdom EMAIL
Most recent similar articles in Demographic Research
Excess mortality associated with HIV: Survey estimates from the PHIA project
Volume 51 - Article 38
| Keywords:
excess mortality,
HIV/AIDS,
mortality
A Bayesian model for age at death with cohort effects
Volume 51 - Article 33
| Keywords:
age at death,
Bayesian approach,
cohort effects,
Italy,
mortality
On the relationship between life expectancy, modal age at death, and the threshold age of the life table entropy
Volume 51 - Article 24
| Keywords:
Gompertz law,
life expectancy,
lifespan variation,
longevity,
mode,
mortality
The role of sex and age in seasonal mortality – the case of Poland
Volume 51 - Article 17
| Keywords:
mortality,
Poland,
seasonality,
sex differences
Data errors in mortality estimation: Formal demographic analysis of under-registration, under-enumeration, and age misreporting
Volume 51 - Article 9
| Keywords:
age misreporting,
data errors,
formal demography,
mortality
Cited References: 16
Download to Citation Manager
PubMed
Google Scholar