Brain Drain and Brain Gain in Russia: Analyzing International Migration of Researchers by Discipline using Scopus Bibliometric Data 1996-2020
BBrain Drain and Brain Gain in Russia:Analyzing International Migration ofResearchers by Discipline using ScopusBibliometric Data 1996-2020
Alexander Subbotin , − − − andSamin Aref − − − Lomonosov Moscow State University, ul. Leninskiye Gory, 1119991 Moscow, Russia [email protected] Laboratory of Digital and Computational Demography,Max Planck Institute for Demographic Research,Konrad-Zuse-Str. 1, Rostock 18057, Germany [email protected]
Abstract.
We study international mobility in academia with a focuson migration of researchers to and from Russia. Using all Scopus publi-cations from 1996 to 2020, we analyze bibliometric data from over halfa million researchers who have published with a Russian affiliation ad-dress at some point in their careers. Migration of researchers is observedthrough the changes in their affiliation addresses. For the first time, weanalyze origins and destinations of migrant researchers with respect totheir fields and performance and compute net migration rates based onincoming and outgoing flows. Our results indicate that while Russia hasbeen a donor country in the late 1990s and early 2000s, it has experi-enced a relatively symmetric circulation of researchers in more recentyears. Using subject categories of publications, we quantify the impactof migration on each field of scholarship. Our analysis shows that Russiahas suffered a net loss in almost all disciplines and more so in neu-roscience, decision sciences, dentistry, biochemistry, and mathematics.For economics and environmental science, there is a relatively balancedcirculation of researchers to and from Russia. Our substantive resultsreveal new aspects of international mobility in academia and its impacton a national science system which speak directly to policy develop-ment. Methodologically, our new approach of handling big data can beadopted as a framework of analysis for studying scholarly migration inother countries.
Keywords:
High-skilled migration · Bibliometric data · Computationaldemography · Science of science · Scientometrics. a r X i v : . [ c s . D L ] A ug A. Subbotin et al.
In the interconnected world, most national science systems cannot be studied ina vacuum disregarding the impact of human mobility and migration. Countriesare indeed affected by international migration of the highly skilled specialistsincluding researchers. Our era has witnessed a large increase in high-skilled mi-gration between countries, which poses new challenges both for researchers andpolicy makers. Russia is not an exception in the global international migrationsystem: a large part of its population is actively on the move for various reasons[14]. Russia is also an attractive destination for some international migrants,especially migrants from former Soviet Union countries [6,30]. Moreover, somemigrants may consider Russia as a transit stop for further migration to othercountries [32]. Previous studies suggest that Russia is both a donor country anda recipient country [11,29] for migration in general. If the characteristics of mi-grants are taken into account, Russia is suggested to be more of a donor country[36,39,17], i.e. a country on the losing side of an international exchange of highlyskilled individuals.The number of researchers and their outputs in Russia are perhaps not aswell-known as those of other developed countries. According to SciVal 2010-2019 data, Russia has over 440,000 researchers (comparable to Australia andItaly) who have produced nearly 700,000 pieces of scholarly publications (com-parable to South Korea). Despite these features, Russia has been a relativelyunder-studied case in the scientometrics literature. Most studies on this topicare limited to qualitative explanations on the emigration of specialists whichoften do not go beyond suggesting the necessity of facilitating circular migrationfor Russia [38,15,24,17,31,37,36,27,34]. Therefore, a deeper analysis is needed toquantitatively study the international movements of researchers in Russia andits implications for different fields of science.According to previous studies, a large number of scientists in mathematics[37,31], physics [5,31], and computer science [31,3] leave Russia. The major des-tination countries for the scholars from Russia are suggested to be the UnitedStates (US), Germany, France, the United Kingdom (UK), and Japan [18]. Themovers are more often from major scientific centers in Moscow, St. Petersburg,Novosibirsk, and Yekaterinburg, and come from lower age groups [7,13], whootherwise have the potential of contributing to the Russian science system fora long time. Further research is needed to accurately quantify this phenomenonwith respect to similarities and differences between all migrant researchers, theirorigin and destination countries and the interplay of their mobility patterns,level of experience, and research performance, and the impact on Russia.Quantitative studies on international migration of researchers seem to becomplicated by a lack of reliable, relevant, and comparable statistics. Recentstudies on this topic use bibliometric data to detect migrant populations amongresearchers and obtain migration trajectories and flows for further analysis [23,4,19,22].This method involves tracking the international movements of researchers throughthe changes in the affiliation addresses. The feasibility of this approach hasbeen tested in previous studies that estimated migration flows among scholars rain Drain and Brain Gain in Russia 3 [23,4,19,22]. In this study, we adopt such a method for focusing on researcherswho have published with a Russian affiliation address at some point in 1996-2020. We track the international movements of all such researchers to analyzethe impact of migration on the Russian science system overall and in differentfields of research.
The availability of millions of publications in the Scopus database (the largestdatabase of peer-reviewed literature [12,25]), allows us to study scholarly migra-tion in Russia by aggregating movements of each researcher who has affiliationties to Russia at some point in 1996-2020 period (up to the end of April 2020).The unit of data is an authorship record which is the linkage between an authoraffiliation and a publication. The data linked to an authorship record provideproxies not only for the geographic locations of researchers, but also for theirresearch areas. Scopus annotates subject codes to more than 25000 indexed pub-lication venues based on the topics they cover. This allows us to analyze thedisciplines of internationally mobile researchers based on the subjects of theirpublications.There are more than 2 million publications in Scopus from over 659’000individual authors who have published with a Russian address at some pointover the 1996-2020 period. After retrieving this data, we focus on the scholarswho also have countries of affiliation other than Russia in their publications. Thisstep excludes those researchers who do not have any evidence of internationalmobility and authors who only have one publication. Given that migration isa rare event, the subset of the data we mostly focus on would be authorshiprecords associated with 522’000 publications from nearly 30’000 internationallymobile researchers.
The Scopus author ID [16,2] allows us to identify authorship records of individualscholars and accordingly detect mobility events. However, there are data qualityissues with Scopus author IDs and affiliations [22] which require some attentionbefore movements can be detected. The affiliations are not standard, and theymay have substantially differ formats. In a large majority of cases, an affiliationaddress has a country while there are 9’701 authors in our dataset who haverecords without a country. These come from 7’279 distinct publications. Inspiredby [22], we use a neural network to predict the missing country information. Theneural network takes affiliation address of an authorship record and predicts thecountry associated with it. For technical details of the development of such analgorithm, one may refer to [22]. We use 1 million records which have countriesas training data (80%) and test data (20%). On 98 .
4% of the test data, the
A. Subbotin et al. neural network predicts the expected country. Ensuring the high accuracy ofthis method, we use the trained neural network for predicting missing countries.Scopus author identification system is suggested to be reliable for analyzingmigration of researchers [2] as most author IDs correctly identify one researchers.However, the Scopus author identification is not perfect: there may be severaldifferent individuals with the same name (or similar names) who are incorrectlyassigned the same author ID. We approach this problem by applying an authordisambiguation process [22] on the authorship records which are more likely tobe impacted by the lack of accuracy in the Scopus author identification system.These records are selected from the extreme values in number of countries andnumber of publications. Authorship IDs which exceed either of two thresholdsbelow are deemed suspicious and will be treated by an author disambiguationmethod. Threshold 1: being associated with more than 6 countries of affiliation.Threshold 2: being associated with more than 292 publications (an average ofmore than one publication per month across a period of 24 years and 4 months).Among more than 659’000 distinct author IDs in our data, 3’563 author IDsare deemed suspicious. They are associated with 334’484 distinct publications(some publications are shared between them). We disambiguate these recordsusing an unsupervised machine learning algorithm [22] inspired by the state-of-the-art methods in the literature [10] and assign revised author IDs using themethod briefly described below. The idea behind the author disambiguation al-gorithm that we use is making pairwise comparisons between every two recordswith the same author ID and allocating scores which are higher if the two au-thorship records share similar traits and lower if they are dissimilar. Then, thescores are summed up and a distance matrix is calculated for all pairs of au-thorship records. Using agglomerative clustering from the scikit-learn packagein Python [28], we obtain clusters of highly similar authorship records. Finally, arevised author ID is issued to each cluster [22]. Implementing this author disam-biguation method to the subset of 3’563 suspicious author IDs, leads to 11’833revised author IDs.
According to All Science Classification Codes (ASJC), there are four majorfields of science: life sciences (including five sub-fields ), social sciences (whichincludes six sub-fields ), physical sciences (including ten sub-fields ), and health (1) agricultural and biological sciences (2) biochemistry, genetics and molecularbiology (3) immunology and microbiology (4) neuroscience and (5) pharmacology,toxicology and pharmaceutics (1) arts and humanities (2) business, management and accounting (3) decisionsciences (4) economics, econometrics and finance (5) psychology and (6) other socialsciences (1) chemical engineering (2) chemistry (3) computer science (4) earth and planetarysciences (5) energy (6) engineering (7) environmental science (8) materials science(9) mathematics and (10) physics and astronomyrain Drain and Brain Gain in Russia 5 sciences (which includes five sub-fields ). Each publication venue in Scopus isclassified by possibly multiple ASJC codes which determine the fields and sub-fields of the topics they cover. At the level of ASJC four major fields of science,we consider that researchers can either belong to one of the four fields or theyare multidisciplinary.We initially compute the frequency ( f ) of each of the four major fields inthe authorship records of each researcher. Then we calculate four Z-scores foreach researcher based on the mean ( µ ) and standard deviation ( σ ) of frequenciesof each major field using Z = ( f − µ ) /σ . Based on the largest Z-score whichexceeds α = 0 . , we group the researchers to one of the four groups of health,life, physical, and social sciences. For 10% of researchers, neither of the Z-scoresexceed the threshold of α , and we group them as multidisciplinary . For analyzing scholarly migration, we borrow well known and fundamental con-cepts such as origin, destination and migrant from migration studies, and re-purpose them for usage in an academic sense. Accordingly, a country of academicorigin is the country appearing in the first publication of a researcher, while thedestination country is determined by the most recent country affiliation. To referto a researcher who have had an international move we use the term academicmigrant (or migrant for brevity). We consider an international mobility event ifthe changes in affiliations across two different years are such that the mode ofaffiliation country changes for a researcher. We define four categories for aca-demic migrants based on their countries of academic origin and destination. Inour analysis, each migrant belongs to one of four categories as follows:(1)
Immigrant (origin: not Russia, destination: Russia),(2)
Emigrant (origin: Russia, destination: not Russia),(3)
Return migrant (origin: Russia, destination: Russia),(4)
Transient (origin: not Russia, destination: not Russia).
At the level of ASJC 26 sub-fields (disciplines), we consider that researchers arepotentially active in and contributing to several of them. Therefore, we use nor-malized contribution to quantify the contribution of a given researcher to differ-ent fields in a normalized way. The normalized contribution
N C j ( d ) of researcher j in discipline d (among a total of n disciplines) is defined and formulated in Eq.(1) based on the relative frequency of discipline d in their authorship records. s jd is the frequency of discipline d in the authorship records of individual j . The de-nominator in Eq. (1) is the sum of frequencies of all disciplines in the authorshiprecords of individual j . (1) medicine (2) nursing (3) veterinary (4) dentistry and (5) health professions Value of α is selected such that only 10% of researchers become multidisciplinary.Stricter limits based on a larger α lead to clearer boundaries between the four mainfields and more individuals belonging to the multidisciplinary group. A. Subbotin et al. N C j ( d ) = s jd (cid:80) ni =1 s ji i = 1 , . . . , n ∀ j ∈ { , . . . , k } (1)As an illustrative example, consider that the authorship records of an indi-vidual over their career are as provided in Table 1. Table 1.
Example authorship records with multiple subjects and countriesAuthor ID DOI ASJC Subject Country YearX 22222 Mathematics Russia 2012X 33333 Chemistry, Energy Russia, US 2013X 44444 Mathematics, Chemistry US 2015
Distinct digital object identifiers (DOIs) in Table 1 show that these author-ship records are associated with three distinct publications. The normalized con-tributions of the researcher, who is identified by Author ID X, are
N C X (chemistry) = 2 / , N C X (energy) = 1 / , N C X (mathematics) = 2 / . To aggregate the normalized contributions for a discipline, the normalizedcount of migrants in discipline d can be used which is calculated by addingup all the normalized contributions of mobile researchers for discipline d asformulated in Eq. (2). The normalized count of migrants in discipline d , denotedas P d , can be thought of as a weighted sum for the population of internationallymobile researchers in discipline d normalized based on giving fractional weightsto individuals depending on how active they are in that discipline compared totheir other disciplines. If the result for P d is decimal we use arithmetic rounding. P d = k (cid:88) j =1 N C j ( d ) (2)Given that each mobile researcher belongs to one of the four categories ofmigrants, normalized counts can similarly be computed based on the normal-ized contributions associated with each type of migrant. Accordingly, we obtain P imm d , P emi d , P ret d , P tra d respectively as normalized populations of immigrants, em-igrants, return migrants, and transients in discipline d . We present the main results of our analysis in this Section which is structuredas follows: Subsection 3.1 outlines the analysis of the geography of mobile re-searchers (common origin and destination countries). Subsection 3.2 concernsthe origin and destination countries with respect to research performance nor-malized by age. Subsection 3.3 presents our estimates of net migration rates (to rain Drain and Brain Gain in Russia 7 evaluate brain circulation in Russia overall and by major fields). Subsection 3.4explores disciplines of mobile researchers (to evaluate the impact of migrationon each field of science in Russia).
Figure 1 illustrates the international paths for researchers to and from Russiaover the 1996-2020 period. The five most common countries of academic originfor immigrants are US, Ukraine, Germany, France, and UK respectively. As des-tinations for emigrants US and Germany are again the most common countriesrespectively, followed by UK and France, while Ukraine is ranked the fifth amongcommon destinations. Moreover, the scale of emigration to frequent destinationsis more than twice the scale of immigration from frequent origins. We can see inFigure 1 that US and Russia are connected by two edges (whose directions areclockwise): blue (scholars moving from US to Russia) and pink (scholars movingfrom Russia to US). The pink edge is thicker than the blue one, which meansthat the researchers leaving Russia for US outnumber people coming from US toRussia. In this context, US, Germany, UK, and France are more likely to be des-tinations than being origins with respect to the imbalanced flows of immigrantsand emigrants.
AngolaAngolaAlbaniaAlbania United Ara...United Ara...ArgentinaArgentina ArmeniaArmenia AustraliaAustraliaAustriaAustria AzerbaijanAzerbaijanBurundiBurundiBelgiumBelgiumBeninBeninBurkina FasoBurkina Faso BangladeshBangladeshBulgariaBulgaria BahrainBahrainBosnia and...Bosnia and... BelarusBelarusBrazilBrazil BruneiBruneiCanadaCanada SwitzerlandSwitzerlandChileChile ChinaChinaIvory CoastIvory Coast CameroonCameroonColombiaColombiaCosta RicaCosta RicaCubaCuba CyprusCyprusCzech Repu...Czech Repu...GermanyGermanyDenmarkDenmarkAlgeriaAlgeriaEcuadorEcuador EgyptEgyptSpainSpain EstoniaEstonia EthiopiaEthiopiaFinlandFinland FijiFijiFalkland I...Falkland I... FranceFrance Micronesia...Micronesia...United Kin...United Kin... GeorgiaGeorgiaGhanaGhanaGuineaGuineaGuadeloupeGuadeloupe GreeceGreeceGrenadaGrenada GreenlandGreenlandFrench Gui...French Gui...GuyanaGuyana Hong KongHong KongCroatiaCroatia HungaryHungary IndonesiaIndonesiaIndiaIndiaIrelandIreland IranIranIraqIraqIcelandIceland IsraelIsraelItalyItalyJamaicaJamaica JordanJordan JapanJapanKazakhstanKazakhstanKenyaKenya KyrgyzstanKyrgyzstan CambodiaCambodia South KoreaSouth KoreaKuwaitKuwait LaosLaosLebanonLebanonLibyaLibyaLiechtenst...Liechtenst... Sri LankaSri LankaLithuaniaLithuaniaLuxembourgLuxembourg LatviaLatvia MacaoMacaoMoroccoMorocco MonacoMonaco Moldova, R...Moldova, R...MexicoMexico North Mace...North Mace...MaliMali MaltaMalta BurmaBurmaMontenegroMontenegro MongoliaMongoliaMozambiqueMozambiqueMalawiMalawi MalaysiaMalaysiaNigeriaNigeriaNetherlandsNetherlandsNorwayNorway New ZealandNew ZealandOmanOman PakistanPakistanPanamaPanamaPeruPeru PhilippinesPhilippinesPolandPolandPuerto RicoPuerto Rico North KoreaNorth KoreaPortugalPortugal Palestinia...Palestinia... QatarQatarRomaniaRomania RussiaRussiaRwandaRwanda Saudi ArabiaSaudi Arabia SingaporeSingaporeSaint Hele...Saint Hele... SerbiaSerbiaSurinameSuriname SlovakiaSlovakiaSloveniaSloveniaSwedenSweden SwazilandSwaziland SeychellesSeychellesSyriaSyria ThailandThailandTajikistanTajikistanTurkmenistanTurkmenistanTunisiaTunisia TurkeyTurkey TuvaluTuvaluTaiwanTaiwanTanzaniaTanzaniaUkraineUkraineUruguayUruguayUnited Sta...United Sta... UzbekistanUzbekistanVenezuelaVenezuela VietnamVietnamSamoaSamoa YemenYemenSouth AfricaSouth AfricaZimbabweZimbabweYugoslaviaYugoslavia
Fig. 1.
Network of movements to and from Russia among researchers over 1996-2020.Directions of edges are clockwise. Common origins and destinations are shown withdistinct colors. Colors of the flows are based on the origin country. Thickness of an edgeis proportional to the flow it represents. See the figure on screen for high resolution.
An exception among the top countries of origin is Ukraine with fewer emi-grants from Russia than immigrants to Russia. The number of immigrants from
A. Subbotin et al.
Ukraine to Russia is 2 . rain Drain and Brain Gain in Russia 9 AngolaAngolaAlbaniaAlbania United Ara...United Ara...ArgentinaArgentina ArmeniaArmenia AustraliaAustraliaAustriaAustria AzerbaijanAzerbaijanBelgiumBelgiumBeninBenin BangladeshBangladeshBulgariaBulgariaBosnia and...Bosnia and... BelarusBelarusBrazilBrazilCanadaCanada SwitzerlandSwitzerlandChileChile ChinaChinaColombiaColombiaCosta RicaCosta RicaCubaCuba CyprusCyprusCzech Repu...Czech Repu...GermanyGermanyDenmarkDenmark EgyptEgyptSpainSpain EstoniaEstonia EthiopiaEthiopiaFinlandFinlandFranceFranceUnited Kin...United Kin... GeorgiaGeorgiaGhanaGhanaGuineaGuineaGuadeloupeGuadeloupe GreeceGreeceGreenlandGreenlandFrench Gui...French Gui... Hong KongHong KongCroatiaCroatia HungaryHungary IndonesiaIndonesiaIndiaIndiaIrelandIreland IranIranIraqIraqIsraelIsraelItalyItaly JapanJapanKazakhstanKazakhstanKenyaKenya KyrgyzstanKyrgyzstan South KoreaSouth KoreaKuwaitKuwait LaosLaosLebanonLebanonLithuaniaLithuaniaLuxembourgLuxembourg LatviaLatviaMoroccoMorocco Moldova, R...Moldova, R...MexicoMexico BurmaBurmaMongoliaMongoliaMalawiMalawi MalaysiaMalaysiaNigeriaNigeriaNetherlandsNetherlandsNorwayNorway New ZealandNew ZealandPolandPolandPortugalPortugal RomaniaRomania RussiaRussiaSaudi ArabiaSaudi Arabia SingaporeSingaporeSerbiaSerbiaSlovakiaSlovakiaSloveniaSloveniaSwedenSweden ThailandThailandTajikistanTajikistanTunisiaTunisia TurkeyTurkey TaiwanTaiwanTanzaniaTanzaniaUkraineUkraineUnited Sta...United Sta... UzbekistanUzbekistanVenezuelaVenezuela VietnamVietnamSouth AfricaSouth AfricaYugoslaviaYugoslavia (a) Health sciences
ArgentinaArgentina ArmeniaArmenia AustraliaAustraliaAustriaAustria AzerbaijanAzerbaijanBelgiumBelgium BangladeshBangladeshBulgariaBulgariaBosnia and...Bosnia and... BelarusBelarusBrazilBrazilCanadaCanada SwitzerlandSwitzerlandChileChile ChinaChinaCameroonCameroonColombiaColombiaCosta RicaCosta Rica CyprusCyprusCzech Repu...Czech Repu...GermanyGermanyDenmarkDenmark EgyptEgyptSpainSpain EstoniaEstoniaFinlandFinlandFalkland I...Falkland I... FranceFranceUnited Kin...United Kin... GeorgiaGeorgiaGreeceGreeceGrenadaGrenada Hong KongHong KongCroatiaCroatia HungaryHungary IndonesiaIndonesiaIndiaIndiaIrelandIreland IranIranIraqIraqIcelandIceland IsraelIsraelItalyItaly JapanJapanKazakhstanKazakhstanKyrgyzstanKyrgyzstan CambodiaCambodia South KoreaSouth KoreaLithuaniaLithuaniaLuxembourgLuxembourg LatviaLatviaMoroccoMorocco Moldova, R...Moldova, R...MexicoMexico MontenegroMontenegro MongoliaMongoliaMalaysiaMalaysiaNetherlandsNetherlandsNorwayNorway New ZealandNew ZealandPakistanPakistanPanamaPanamaPeruPeru PhilippinesPhilippinesPolandPolandPuerto RicoPuerto Rico PortugalPortugal RomaniaRomania RussiaRussiaSaudi ArabiaSaudi Arabia SingaporeSingaporeSerbiaSerbiaSlovakiaSlovakiaSloveniaSloveniaSwedenSweden ThailandThailandTajikistanTajikistanTurkmenistanTurkmenistanTurkeyTurkey TaiwanTaiwanUkraineUkraineUnited Sta...United Sta... UzbekistanUzbekistan VietnamVietnamYemenYemenSouth AfricaSouth Africa (b) Life sciences
AngolaAngola United Ara...United Ara...ArgentinaArgentina ArmeniaArmenia AustraliaAustraliaAustriaAustria AzerbaijanAzerbaijanBurundiBurundiBelgiumBelgiumBeninBeninBurkina FasoBurkina Faso BangladeshBangladeshBulgariaBulgaria BahrainBahrainBosnia and...Bosnia and... BelarusBelarusBrazilBrazil BruneiBruneiCanadaCanada SwitzerlandSwitzerlandChileChile ChinaChinaIvory CoastIvory Coast CameroonCameroonColombiaColombiaCosta RicaCosta RicaCubaCuba CyprusCyprusCzech Repu...Czech Repu...GermanyGermanyDenmarkDenmarkAlgeriaAlgeriaEcuadorEcuador EgyptEgyptSpainSpain EstoniaEstonia EthiopiaEthiopiaFinlandFinlandFranceFranceUnited Kin...United Kin... GeorgiaGeorgiaGhanaGhanaGuineaGuinea GreeceGreeceGuyanaGuyana Hong KongHong KongCroatiaCroatia HungaryHungary IndonesiaIndonesiaIndiaIndiaIrelandIreland IranIranIraqIraqIcelandIceland IsraelIsraelItalyItalyJamaicaJamaica JordanJordan JapanJapanKazakhstanKazakhstanKyrgyzstanKyrgyzstan South KoreaSouth KoreaKuwaitKuwaitLibyaLibyaLiechtenst...Liechtenst... Sri LankaSri LankaLithuaniaLithuaniaLuxembourgLuxembourg LatviaLatvia MacaoMacaoMoroccoMoroccoMonacoMonaco Moldova, R...Moldova, R...MexicoMexico North Mace...North Mace...MaliMali BurmaBurmaMontenegroMontenegro MongoliaMongoliaMozambiqueMozambique MalaysiaMalaysiaNigeriaNigeriaNetherlandsNetherlandsNorwayNorway New ZealandNew ZealandOmanOman PakistanPakistanPolandPolandPuerto RicoPuerto Rico North KoreaNorth KoreaPortugalPortugal Palestinia...Palestinia... QatarQatarRomaniaRomania RussiaRussiaRwandaRwanda Saudi ArabiaSaudi Arabia SingaporeSingaporeSaint Hele...Saint Hele... SerbiaSerbiaSurinameSuriname SlovakiaSlovakiaSloveniaSloveniaSwedenSweden SeychellesSeychellesSyriaSyria ThailandThailandTajikistanTajikistanTurkmenistanTurkmenistanTunisiaTunisia TurkeyTurkey TaiwanTaiwanTanzaniaTanzaniaUkraineUkraineUruguayUruguayUnited Sta...United Sta... UzbekistanUzbekistanVenezuelaVenezuela VietnamVietnamSamoaSamoa YemenYemenSouth AfricaSouth AfricaYugoslaviaYugoslavia (c) Physical sciences
United Ara...United Ara...ArmeniaArmenia AustraliaAustraliaAustriaAustria AzerbaijanAzerbaijanBelgiumBelgium BulgariaBulgaria BahrainBahrainBelarusBelarusBrazilBrazil BruneiBruneiCanadaCanada SwitzerlandSwitzerland ChinaChinaColombiaColombiaCubaCuba CyprusCyprusCzech Repu...Czech Repu...GermanyGermanyDenmarkDenmarkEcuadorEcuador EgyptEgyptSpainSpain EstoniaEstoniaFinlandFinlandFranceFrance Micronesia...Micronesia...United Kin...United Kin... GeorgiaGeorgiaGreeceGreece Hong KongHong KongCroatiaCroatia HungaryHungary IndonesiaIndonesiaIndiaIndiaIrelandIreland IranIranIcelandIceland IsraelIsraelItalyItalyJamaicaJamaica JapanJapanKazakhstanKazakhstanKenyaKenya KyrgyzstanKyrgyzstan South KoreaSouth KoreaLithuaniaLithuaniaLuxembourgLuxembourg LatviaLatvia MacaoMacaoMoldova, R...Moldova, R...MexicoMexico North Mace...North Mace...MaltaMalta MongoliaMongoliaMalaysiaMalaysiaNigeriaNigeriaNetherlandsNetherlandsNorwayNorway New ZealandNew ZealandPakistanPakistanPolandPolandPortugalPortugal RomaniaRomania RussiaRussiaSaudi ArabiaSaudi Arabia SingaporeSingaporeSerbiaSerbiaSlovakiaSlovakiaSloveniaSloveniaSwedenSweden SwazilandSwaziland ThailandThailandTajikistanTajikistanTurkmenistanTurkmenistanTurkeyTurkey TuvaluTuvaluTaiwanTaiwanUkraineUkraineUnited Sta...United Sta... UzbekistanUzbekistan VietnamVietnamSouth AfricaSouth AfricaZimbabweZimbabwe (d) Social sciences
Fig. 2.
Migration flows among researchers in four major fields. Colors of the flows arebased on the origin country. See the figure on screen for high resolution.0 A. Subbotin et al.
It can be seen in Figure 2 that Physical sciences has generally the largest flowsfollowed by life sciences, health sciences, and social sciences in decreasing order.The top five destination countries in Figure 2 are among US, Germany, UK,Ukraine, France, and Kazakhstan while their order sometimes changes depend-ing on the major field of science. US is always the most common destination. Forlife and health sciences, the second country is Germany followed by UK in thethird place while for social sciences their order is reversed. For physical sciences,the second and third common destinations are Germany and Ukraine respec-tively. For US, Germany, UK, and France, the flow to Russia (disaggregated bythe major field) is smaller than the respective flows in the opposite direction.However, in social sciences, the flows from US and UK to Russia are larger thanthe respective flows from Russia to these two countries. For Ukraine, in all fourmajor fields, the flows to Russia is larger than the respective flows from Russia.
We also look at common origins and destinations taking into account citationsand academic age (the number of years since first publication [4]) of researchers.We calculate an annual citation rate by dividing the a researcher’s total numberof citations (as of 2020) by their academic age. It should be noted that thereare disparities in citations between immigrants and emigrants and by fields ofscience. In table 3.2, the averages and standard deviations of annual citationrates are provided. We can see that the rates for emigrants are generally higherthan those of the immigrants, with the exception of social sciences. This suggeststhat in most major fields, internationally mobile researchers who come to Russiaperform lower than those leaving Russia in terms of total citations receivedcontrolling for the differences in years of academic experience (academic age).
Table 2.
Average and standard deviation of annual citation by field and migrant typeLife sci. Social sci. Physical sci. Health sci. MultidisciplinaryImmigrants 37 . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . Among fields of science, there are substantial variations in citations pat-terns (the mean values are reported in the brackets): life sciences (58.5), socialsciences (9.45), physical sciences (89.8), health sciences (55.9), and multidis-ciplinary (69.0). To obtain a citation-based measure of performance, annualcitation of a researcher should be divided by the average of their field. Afternormalization, we identify three groups of migrants: lowly cited migrants (thefield-normalized annual citation rate is less than 0 . moderately cited migrants (the field-normalized annual citation rate is between 0 .
09 and 0 . highly citedmigrant (the field-normalized annual citation rate is above 0 . rain Drain and Brain Gain in Russia 11 KORNLDUZBITAPOLCANCHNJPNBLRKAZGBRFRADEUUKRUSA 0 200 600 1000 1400 o r i g i n number of immigrants (a) Lowly cited immigrants CHEINDNORFINNLDBLRCANITASWEJPNGBRFRAUKRDEUUSA 0 100 200 300 400 o r i g i n number of immigrants (b) Moderately cited immi. SRBNLDFINBLRCANJPNESPCHESWEITAUKRGBRFRADEUUSA 0 50 100 150 200 o r i g i n number of immigrants (c) Highly cited immigrants SWEISRJPNVNMNLDCHEBLRCHNCANFRAKAZGBRUKRDEUUSA 0 500 1000 1500 2000 de s t i na t i on number of emigrants (d) Lowly cited emigrants BELJPNAUTISRAUSITANLDFINSWECHECANFRAGBRDEUUSA 0 200 600 1000 1400 de s t i na t i on number of emigrants (e) Moderately cited emig. JPNBELAUTESPITAAUSNLDFINCHECANSWEFRAGBRDEUUSA 0 200 600 1000 1400 de s t i na t i on number of emigrants (f) Highly cited emigrants Fig. 3.
Top 15 origins for lowly (a), moderately (b), and highly cited (c) immigrants,and top 15 destinations for lowly (d), moderately (e), and highly cited (f) emigrants.
In Figure 3, it can be seen that US is on the lead as the most common originof immigrants. Germany is more common among moderately and highly citedimmigrants, while Ukraine is a more common origin among lowly cited immi-grants (Subfigures 3a-3c). We can also see that some former Soviet Union coun-tries (Ukraine, Kazakhstan, Belarus, Uzbekistan) appear in the top 15 originsfor lowly cited immigrants, while only Ukraine and Belarus also appear amongthe most common countries of immigrants performing better with respect tocitations.US and Germany are the two most common destinations for all emigrantsregardless of their citation-based performance (Subfigures 3d-3f). Ukraine, Kaza-khstan, and Belarus are among the top destinations of lowly cited emigrantswhile none of these countries is common for moderately or highly cited emi-grants. UK, France, and Canada also appear as common destinations for allcategories of emigrants.
Migration rates are commonly used measures of the difference between move-ments into and out of a certain area [21]. Net migration rate for a given arearefers to the difference between in-migration and out-migration rates per 1000people. A positive value means more people entering than leaving a given area.Using I y and E y to represent the number of scholars who have immigrated toRussia and emigrated from Russia respectively during year y , and M y to repre-sent Russia’s population of scholars at the beginning of year y , the net migrationrate N M R y can be calculated according to Eq. 3.In-migration and out-migration rates can be computed based on I y /M y and E y /M y respectively which only take one direction of the flows into consideration.In Eq. 3, the denominator, M y , is obtained from the original superset of ourbibliometric data (which includes non-movers as well). It estimates the totalnumber of researchers in Russia in year y based on the affiliation addressesassociated with publication dates within a two year vicinity of year y . For thisestimation, we assume that the researchers with Russian addresses have beenin the country two years before and two years after the publication year unlessthere are evidence to the contrary (publications showing other countries for theresearcher). N M R y = ( I y − E y ) × / ( M y ) (3)Subfigure 4a illustrates net migration rate in Russia over the 1998-2018 pe-riod. The lowest value of net migration rate is observed for 2001, and it was − . . . . . − . We further analyze movements of migrant scholars with respect to the 26 sub-fields of ASJC assocaited with their authorship records to find the the impactof migrations on different disciplines. We develop a measure inspired by netmigration rate for each discipline to find the extent to which a given disciplinein Russia is impacted by the imbalance of incoming and outgoing flows. Tooperationalize this idea, we start with the concepts of normalized contributionand normalized count formulated in Eqs. (1) and (2) discussed in Section 2. Weevaluate the possible losses in each field by looking at the relative difference rain Drain and Brain Gain in Russia 13 −10−50
NMR (a) Net migration rate . . . . . . . . . . . . . . . . . . . Field
Health Life Social Multidisciplinary Physical (b) In-migration rate . . . . . . . . . . . . . . . . . . . . Field
Health Life Social Multidisciplinary Physical (c) Out-migration rate
Fig. 4.
Net migration (a), in-migration (b) and out-migration (c) rates per 1000 re-searchers in Russia over the 1998-2018 period between the normalized counts of immigrants, return migrants, emigrants, andtransients using a parsimonious measure to quantify
Field-based net brain drain ( F N BD d ) formulated in Eq. 4. Emigrants and transients increase the net drainof a national science system and therefore they have positive coefficients in Eq. 4.In contrast, we consider negative coefficients for immigrants and return migrantsin Eq. 4 because these groups of migrants decrease the net drain of a nationalscience system. A larger positive value of F N BD d means a larger loss due to theimbalance of migration flows in discipline d . The largest (smallest) value possiblefor F N BD d is 1 (-1) which is associated to a hypothetical situation where allmigrants in discipline d are emigrants and transients (immigrants and returnmigrants) and the brain drain (brain gain) is therefore at its peak. Each term ofEq. 4 represents the impact of the respective group of migrants. F N BD d = ( P emi d /P d ) + ( P tra d /P d ) − ( P imm d /P d ) − ( P ret d /P d ) (4)To illustrate the application of F N BD d , we use the discipline of computerscience as an example. We obtain contributions of migrants to different fieldsusing Eq. (1) and sum up the normalized contributions in the field of computerscience for all four types of migrants (calculating P imm d , P emi d , P ret d and P tra d ) andall migrants together (calculating P d ) using Eq. 2. Accordingly, the normalizedcount of mobile researchers in computer science would be P d = 1329 whichincludes all four types of migrants. Then, we use the formula in Eq. 4 to calculate F N BD for computer science which is equal to 0 . .
5% net drain in the field of computer science. Figure 5 shows the four terms of
F N BD and its total value for all disciplines. We can see in Figure 5 that Russia terms of FNBD pertaining to each migrant type FNBD (total) − . . . . .
00 0 .
05 0 .
10 0 .
15 0 .
20 0 . Economics, Econometrics and FinanceEnvironmental ScienceAgricultural and Biological SciencesBusiness, Management and AccountingEarth and Planetary SciencesEnergySocial SciencesEngineeringImmunology and MicrobiologyArts and HumanitiesPhysics and AstronomyHealth ProfessionsPsychologyComputer ScienceMedicineVeterinaryNursingMaterials ScienceChemical EngineeringPharmacology, Toxicology and PharmaceuticsChemistryMathematicsBiochemistry, Genetics and Molecular BiologyDentistryDecision SciencesNeuroscience d i sc i p li ne Immigrants Emigrants Return migrants Transients Total value of FNBD
Fig. 5.
Field-based net brain drain for different categories of migrants has suffered a large loss in disciplines such as neuroscience (24.5%), decisionsciences (21.6%), dentistry (20.0%), biochemistry (18.7%), mathematics (18.2%),chemistry (17.7%), pharmacology (15.9%), chemical engineering (15.4%), andmaterials science (15.3%). For almost all other disciplines,
F N BD values showa loss, but to a smaller degree. Interestingly, the values of
F N BD is close tozero suggesting a relatively balanced circulation of flows in economics (-1.3%)and environmental science (1.3%).The results indicate that there is heterogeneity in the impact of internationalmobility of researchers on the fields of scholarship in Russia. This observationcasts doubt on viewing a national science system as just one unit with a simplepositive/negative response to international mobility, instead, the componentsof such a system could be differently impacted by the balance of migrationflows or lack thereof. Note that, if we only consider terms of
F N BD pertainingto immigrants and emigrants, the alternative measurements also show Russiasuffering a net loss in all disciplines because emigrants outnumber immigrants (anexception is economics that has the opposite pattern). However, return migrantsand transients account for considerable proportions of migrations and thereforeexcluding them from the analysis could be contestable. rain Drain and Brain Gain in Russia 15
A major limitation of this study, as well as a remarkable merit, is the use of bib-liometric data, and the unique view they provide. The time required to conductand publish research is an important factor [9] to keep in mind when interpretingthe temporal component of the results on mobility patterns observed throughbibliometric data. Also, it is noted that a one-time usage of an affiliation is notguaranteed to show a direct attachment to the country of affiliation [20]. Ourconservative approach resolves this issue by detecting international moves onlyif the modal country of affiliation changes across different years. Moreover, wecannot observe and track any migration events that are not represented in pub-lications indexed in Scopus. Bibliometric databases could be biased, and therecould be an under-representation of some countries, scientific fields, and lan-guages [12,25,33]. Also, given the fact that we are investigating a specific periodof time, our data suffers from left-truncation.Despite these limitations, bibliometric data facilitate a study on migration ofresearchers to cross the disciplinary boundaries and become more contemporaryand extensive compared to what traditional data sources may allow [1]. Thisstudy makes several contributions to the literature: both methodological contri-butions in usage of bibliometric data and substantive contributions to the studyof scholarly migration in Russia. A missing piece of the puzzle for understandingacademic brain drain has been key migration statistics which our study providesfor the first time for Russia; a commonly debated country of brain drain despitebeing under-explored by quantitative analysis.
In this study, we used affiliation addresses from Scopus publications over 1996-2020 to present a comprehensive and detailed picture of academic migration inRussia. Our goal was to understand the patterns of scholarly migration by track-ing the international movements of researchers and to identify the impact of suchmovements on the Russian science system overall and in each field of scholar-ship. The use of large-scale bibliometric data from Scopus allowed us to achievethis goal in a cross-disciplinary study of demography and scientometrics.In thisstudy, we analyzed international mobility of scholars in Russia by different mi-gration patterns with respect to their countries of origin and destination andtheir disciplines. We aim to extend this study to additional dimensions of anal-ysis such as gender and level of experience as well as more detailed measures forperformance and research quality and quantity for migrating researchers.Our analysis of four categories of academic migrants revealed the similaritiesbetween their common countries of academic origin and destination while con-trasting their differences in migration patterns and the impact on the Russianscience system. US and Germany are the largest scientific hubs linked to Rus-sia. Ukraine turned out to be one of the main donors of researchers to Russia,which could be partly explained by the patterns of Russo-Ukrainian migration in the general population. Using data on major fields of science, we made com-parisons between the international flows in health sciences, life sciences, physicalsciences, and social sciences. Physical sciences has the largest flows followed bylife sciences which seem to be the major fields where most technical knowledgeand skills could be more easily transferable in different parts of the world com-pared to health sciences which may involve bodies of knowledge varying acrosscountries (e.g. national medical protocols) and social sciences which may dependmore on the language, culture, and context of societies.We also analyzed citation data and observed large disparities between citation-based performance of migrants by their types and across different fields. Con-sistent with the generally observed pattern in scientometrics, the two fields ofphysical sciences have the most citations respectively followed by life sciences andhealth sciences (which are somewhat comparable) and social sciences (which hasthe lowest citations). Comparing by migrant types, emigrants from Russia havesubstantially higher citations compared to immigrants to Russia in all fields ex-cept for social sciences. This disparity could be the effect of a combination ofreasons including the research performance of immigrants and emigrants andthe difference in research opportunities in destination countries. Grouping themigrant researchers into three categories based on citation (normalized by ageand field), we compared the origin countries of immigrants as well as destinationcountries of emigrants. As origins, Ukraine, Kazakhstan, Belarus, and Uzbek-istan are more common among lowly cited immigrants. Similarly, Ukraine, Kaza-khstan, and Belarus are ranked higher as destinations of lowly cited emigrants.Using net migration rate, it was shown that while in the late 1990s and early2000s Russia has been overall on the losing side of a brain circulation system,this has not been the case in recent years when net migration rate has beeneven positive for some years and generally oscillates around zero indicating abalance between the annual flows of incoming and outgoing researchers. Ourresults indicate that the overall lack of balance between incoming and outgoingflows of researchers has improved for Russia, but it could be still too early tocall Russia a country of attraction for researchers.As for analyzing the disciplines of migrating researchers, we introduced nor-malized measures for contributions of individuals in different fields. We alsointroduced a measure of net brain drain for quantifying the impact of interna-tional migration on each specific field of science. The analysis showed that overthe time period 1996-2020, there has been a relatively large outflow of specialistsin most fields of scholarship in Russia. Our results indicate that researchers leav-ing Russia in the fields of neuroscience, decision sciences, dentistry, biochemistry,mathematics, chemistry, pharmacology, chemical engineering, and materials sci-ence outnumber those who come to Russia supporting previous findings thatspecialists in these fields are actively leaving Russia [37,5,32,31,3] and going afew steps further by considering incoming flows and all fields of science.The results that this research substantiated for the first time are generaliz-able within the limitations of bibliometric data which were discussed in Section4. Keeping the possible caveats in mind, our substantive and methodological rain Drain and Brain Gain in Russia 17 contributions can be used in furthering our understanding of international mi-gration in academia.For the specific case of this study, timely and detailed statis-tics were required. Our findings revealed new insights for Russia which is hopedto be used in policy development involving highly qualified professionals. Themethodological contribution of this study can be applied to other countries as aframework of analysis to examine other national science systems and the impactof international mobility of researchers on them. This study has only scratchedthe surface on the study of migration among researchers and a new applicationof bibliometric data while many questions are yet unanswered for which we hopeto have paved the way.
Acknowledgements
Authors highly appreciate the technical support from Tom Theile and discussionswith Ebru Sanliturk and Emilio Zagheni which helped improving the article.This study has received access to the bibliometric data through the project“Kompetenzzentrum Bibliometrie” and the authors acknowledge their funderBundesministerium f¨ur Bildung und Forschung (funding identification number01PQ17001).
References
1. Alburez-Gutierrez, D., Aref, S., Gil-Clavel, S., Grow, A., Negraia, D., Zagheni,E.: Demography in the digital era: New data sources for population research. In:Arbia, G., Peluso, S., Pini, A., Rivellini, G. (eds.) Book of Short Papers SIS2019.pp. 23–30. Pearson (2019). https://doi.org/10.31235/osf.io/24jp72. Aman, V.: Does the Scopus author ID suffice to track scientific international mo-bility? A case study based on Leibniz laureates. Scientometrics (2), 705–720(2018), publisher: Springer3. Antoshchuk, I.: Female computer scientists from Post-Soviet space: migration andacademic career in the UK. In: 23rd International Conference on Science and Tech-nology Indicators (STI 2018), September 12-14, 2018, Leiden, The Netherlands.Centre for Science and Technology Studies (CWTS) (2018)4. Aref, S., Zagheni, E., West, J.: The demography of the peripatetic researcher:Evidence on highly mobile scholars from the Web of Science. In: InternationalConference on Social Informatics. pp. 50–65. Springer (2019)5. Ball, D.Y., Gerber, T.P.: Russian Scientists and Rogue States: Does Western Assis-tance Reduce the Proliferation Threat? International Security (4), 50–77 (2005)6. Bedrina, E., Tukhtarova, Y., Neklyudova, N.: Migration from Uzbekistan to Russia:Push-Pull Factor Analysis. In: The International Science and Technology Confer-ence ”FarEastCon”. pp. 283–296. Springer (2018)7. Chepurenko, A.: The role of foreign scientific foundations’ role in the cross-border mobility of Russian academics. International Journal of Manpower (4),562–584 (Jul 2015). https://doi.org/10.1108/IJM-11-2013-0256,
8. Cipko, S.: Contemporary migration from Ukraine. In: Migration Perspectives East-ern Europe and Central Asia, pp. 117 – 132. International Organization for Migra-tion (2006)8 A. Subbotin et al.9. Cohen, P.N.: Scholarly Communication in Sociology. Open Sociology (Mar2019). https://doi.org/10.21428/4388219e, https://opensociology.pubpub.org/pub/scis , publisher: PubPub10. D’Angelo, C.A., van Eck, N.J.: Collecting large-scale publication data at the levelof individual researchers: a practical proposal for author name disambiguation.Scientometrics pp. 1–25 (2020), publisher: Springer11. Di Bartolomeo, A., Makaryan, S., Weinar, A.: Regional Migration Report: Russiaand Central Asia. Tech. rep., European University Institute (2014)12. Falagas, M.E., Pitsouni, E.I., Malietzis, G.A., Pappas, G.: Comparison of pubmed,scopus, web of science, and google scholar: strengths and weaknesses. The FASEBjournal (2), 338–342 (2008)13. Iontsev, V.A., Magomedova, A.G.: Demographic aspects of the development ofhuman capital in Russia and its regions. R-Economy. 2015. Vol. 1. Iss. 3 (3),467–477 (2015)14. Iontsev, V.A., Ryazantsev, S.V., Iontseva, S.V.: Emigration from Russia: newtrends and forms. R-Economy. 2016. Vol. 2. Iss. 2 (2), 216–224 (2016)15. Iontsev, V.A., Zimova, N.S., Subbotin, A.A.: The Problems of “Brain Drain” inRussia and Member States of the Eurasian Economic Union (EAEU). RUDN Jour-nal of Economics (4), 510–517 (2017)16. Kawashima, H., Tomizawa, H.: Accuracy evaluation of Scopus Author ID based onthe largest funding database in Japan. Scientometrics (3), 1061–1071 (2015),publisher: Springer17. Kolesnikova, J., Camille, R., Kamasheva, A., Yue, Z.: Current Trends of Realiza-tion of the Intellectual Capital and Problems of Intellectual Migration. ProcediaEconomics and Finance , 326–332 (Jan 2014). https://doi.org/10.1016/S2212-5671(14)00720-5,
18. Korobkov, A., Zaionchkovskaia, Z.: Russian brain drain: Myths v. re-ality. Communist and Post-Communist Studies , 327–341 (Sep 2012).https://doi.org/10.1016/j.postcomstud.2012.07.01219. Kosyakov, D., Guskov, A.: Impact of national science policy on academicmigration and research productivity in Russia. Procedia Computer Sci-ence , 60–71 (2019),
20. Kosyakov, D., Guskov, A.: Synchronous scientific mobility and international col-laboration: case of Russia. In: Proceedings of the 17th International Conference onScientometrics. pp. 1319–1328 (2019)21. Lieberson, S.: The Interpretation of Net Migration Rates. Sociological Method-ology , 176–190 (1980). https://doi.org/10.2307/270863,
22. Miranda-Gonz´alez, A., Aref, S., Theile, T., Zagheni, E.: Scholarly mi-gration within Mexico: Analyzing internal migration among researchersusing Scopus longitudinal bibliometric data. EPJ Data Science (2020).https://doi.org/10.1140/epjds/s13688-020-00242-x, (in press)23. Moed, H.F., Halevi, G.: A bibliometric approach to tracking interna-tional scientific migration. Scientometrics (3), 1987–2001 (Dec 2014).https://doi.org/10.1007/s11192-014-1307-6, http://link.springer.com/10.1007/s11192-014-1307-6
24. Molodikova, I.N., Yudina, T.N.: Migration strategies of Ukrainian migrants: EU orRussia. Contemporary Problems of Social Work (3), 62–71 (2016)rain Drain and Brain Gain in Russia 1925. Mongeon, P., Paul-Hus, A.: The journal coverage of Web of Science and Scopus: acomparative analysis. Scientometrics (1), 213–228 (2016), publisher: Springer26. Mukomel, V.: Migration of Ukrainians to Russia in 2014–2015. Discourses andPerceptions of the Local Population. Migration and the Ukraine Crisis p. 105(2017), publisher: E-International Relations Bristol27. Naumova, T.V.: Russia’s ”brain drain”. Russian Social Science Review (2), 49–56 (1998), publisher: Taylor & Francis28. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: Machine learn-ing in Python. the Journal of machine Learning research , 2825–2830 (2011),publisher: JMLR. org29. Podolskaya, T., Medyakova, E., Kolesnikova, N.: Migration Policy and Problemsof Ensuring Economic Security of Countries (2020). https://doi.org/10.4018/978-1-7998-0111-5.ch004, (3),80–88 (2013), publisher: Taylor & Francis32. Rybakovsky, L., Ryazantsev, S.: International migration in the Russian Federation.In: UN Expert Group Meeting on International Migration and Development, NewYork, July (2005)33. Sugimoto, C.R., Larivi`ere, V.: Measuring research: What everyone needs to know.Oxford University Press (2018)34. Taylor, R.G., Mechitov, A.I., Schellenberger, R.E.: Transformation within the Rus-sian academic community. International Education (1), 29 (1996), publisher:College of Education, University of Tennessee35. Titarenko, L.: The Republic of Belarus: Flows and Tendencies in Migration Pro-cesses. Borders, Migration and Regional Stability in the EU’s Eastern Neighbour-hood p. 172 (2016)36. Ushkalov, I.G., Malakha, I.A.: The “Brain Drain” as a global phenomenon and itscharacteristics in Russia. Russian Social Science Review (5), 79–95 (2001)37. Volz, E.: Utechka Umov: The History, Implications, and Solutions concerning Rus-sia’s Post-Soviet Brain Drain. Journal of Undergraduate Research (Rochester)(2002)38. Yurevich, M.A., Malakhov, V.A., Aushkap, D.S.: Global Experience in Interactionwith Compatriot Scientists: Lessons for Russia. Herald of the Russian Academy ofSciences (4), 342–350 (Jul 2019). https://doi.org/10.1134/S1019331619040129, https://doi.org/10.1134/S1019331619040129
39. Zubova, L.G.: The human potential of Russian science. Herald of the RussianAcademy of Sciences82