Alexey Bessudnov’s research while affiliated with University of Exeter and other places

In this research note, we used excess deaths among young males to estimate the number of Russian fatalities in the Russo-Ukrainian war in 2022–2023. We based our calculations on the official mortality statistics, split by age and sex. To separate excess deaths due to war from those due to COVID-19, we relied on the ratio of male to female deaths and extrapolated the 2015–2019 trend to get the baseline value for 2022–2023. We found noticeable excess male mortality in all age groups between 15 and 49, with 58,500 ± 2,500 excess male deaths in 2022–2023 (20,600 ± 1,400 in 2022 and 37,900 ± 1,500 in 2023). These estimates were obtained after excluding all HIV-related deaths that showed complex dynamics unrelated to the war. Depending on the modeling assumptions, the estimated number of deaths over the two years varied from about 46,600 to about 64,100, with 58,500 corresponding to our preferred model. Our estimate should be treated as a lower bound on the true number of deaths because the data do not include either the Russian military personnel missing in action and not officially declared dead or the deaths registered in the Ukrainian territories annexed in 2022.

In this research note, we used excess deaths among young males to estimate the number of Russian fatalities in the Russo-Ukrainian war in 2022--23. We based our calculations on the official mortality statistics, split by age and gender. To separate excess deaths due to war from those due to Covid-19, we relied on the ratio of male to female deaths, and extrapolated the 2015--19 trend to get the baseline value for 2022--23. We found noticeable excess male mortality in all age groups between 15 and 49, with 58,500±2,500 excess male deaths in 2022--23 (20,600±1,400 in 2022 and 37,900±1,500 in 2023). These estimates were obtained after excluding all HIV deaths that showed complex dynamics unrelated to the war. Depending on the modelling assumptions, the estimated number of deaths over the two years varied from about 46,600 to about 64,100, with 58,500 corresponding to our preferred model. Our estimate should be treated as a lower bound on the true number of deaths as the data do not include either the Russian military personnel missing in action and not officially declared dead, or the deaths registered in the Ukrainian territories annexed in 2022.

In this paper, we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity was coded from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, with the data sets that have personal names but not ethnicity.

In this paper we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity has been determined from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, in particular with VK and other social media data.

In this paper, we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity was coded from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, with the data sets that have personal names but not ethnicity.

Across most Western societies, trends towards increased ethnic intermarriage have been observed across the second half of the twentieth century. Whether such trends hold across the multi-ethnic society of Russia is not known. We analyze Russian census data and describe levels and trends in ethnic intermarriage in four highly different Russian cities. We find no change in ethnic intermarriage in Moscow, but more intermarriage in younger cohorts in the other three cities where the populations are more ethnically heterogeneous. Levels and trends in ethnic intermarriage vary substantially throughout Russia by locality and ethnic group. Our study highlights how trends in intermarriage can vary within a society, and how the local, historical context may play an important role.

Background: Across most Western societies, trends towards increased ethnic intermarriage have been observed across the second half of the 20th century. Whether such trends hold across the multi-ethnic society of Russia is not known.Objective: We describe levels and trends in ethnic intermarriage rates in four highly different regions of Russia.Methods: We analyse census data from Moscow, Kazan, Makhachkala, Vladikavkaz, calculate odds ratios for ethnic intermarriage and fit log-linear and log-multiplicative models to test for trends in intermarriage. We use age as a proxy for marriage/cohabitation cohorts. Results: We find no change in ethnic intermarriage in Moscow, but more intermarriage in younger cohorts in the other three cities. However, in Kazan and Vladikavkaz the trend is towards more intermarriage between Russians and Tatars, and between Russians and Ossetians, respectively, while in Makhachkala, where there are few ethnic Russians, the trend is towards more intermarriage between indigenous Muslim peoples. Conclusions: Levels and trends in ethnic intermarriage vary substantially throughout Russia by locality and ethnic group. There is no evidence for a trend towards increased intermarriage in Moscow. Contribution: We provide new insight into ethnic intermarriage in Russia. More generally, our study highlights how trends in intermarriage can vary within a society, and how the local, historic context may play an important role.

In the past few decades, several countries have introduced reforms aimed at increasing school autonomy. We evaluate the effect of the introduction of autonomous academies in England on the educational trajectories of children with special educational needs. This has been done using longitudinal data on all schoolchildren in state schools in England, from the National Pupil Database. The results show that the effects of school autonomy on educational inclusion are not uniform and depend on schools’ previous performance and socio‐economic composition. Schools that obtained autonomy under the control of an external sponsor (sponsored academies) were more likely to decrease the proportion of pupils with special needs and remove additional support for them. We do not observe these effects in the schools that voluntarily applied for the more autonomous status (converter academies).

Field experiments have provided ample evidence of ethnic and racial discrimination in the labour market. Less is known about how discrimination varies in multi-ethnic societies, where the ethnic composition of populations is different across locations. Inter-group contact and institutional arrangements for ethnic minorities can mitigate the sense of group threat and reduce discrimination. To provide empirical evidence of this, we conduct a field experiment of ethnic discrimination in Russia with a sample of over 9,000 job applications. We compare ethnically homogeneous cities and cities with ethnically mixed populations and privileged institutional status of ethnic minorities. We find strong discrimination against visible minorities in the former but much weaker discrimination in the latter. These findings demonstrate how institutions and historical contexts of inter-group relations can affect ethnic prejudice and discrimination.