brain drain and brain gain in russia: analyzing ... · ies suggest that russia is both a donor...

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MPIDR Working Paper WP 2020-025 l May 2020https://doi.org/10.4054/MPIDR-WP-2020-025

Alexander Subbotin l [email protected] Aref l [email protected]

Brain Drain and Brain Gain in Russia: Analyzing International Mobility of Researchers by Discipline using Scopus Bibliometric Data

Brain Drain and Brain Gain in Russia:Analyzing International Mobility of Researchersby Discipline using Scopus Bibliometric Data

Alexander Subbotin1,2[0000−0001−5016−0473] and SaminAref2[0000−0002−5870−9253]

1 Lomonosov Moscow State University, ul. Leninskiye Gory, 1119991 Moscow, [email protected]

2 Max Planck Institute for Demographic Research, Konrad-Zuse-Str. 118057 Rostock, [email protected]

Abstract. We study international mobility in academia with a focuson migration of researchers to and from Russia. Using millions of Sco-pus publications from 1996 to 2019, we analyze detailed records of morethan half a million researchers who have published with a Russian affili-ation address at some point in their careers. Migration of researchers isobserved through the changes in their affiliation addresses. We computenet migration rates based on incoming and outgoing flows of researcherswhich indicate that while Russia has been a donor country in the late1990s and early 2000s, in more recent years, it has experienced rela-tively balanced flows and a symmetric circulation of researchers. Usingsubject categories of publications, we obtain a profile of possibly mixeddisciplines for each researcher. This allows us to quantify the impact ofmigration on each field of science. For a country assumed to be losingscientists, our analysis shows that while Russia has suffered a net lossin most disciplines and more so in pharmacology, agriculture, environ-mental science, and energy, it is actually on the winning side of a braincirculation system for dentistry, psychology, and chemistry. For the dis-cipline of nursing, there is a balanced circulation of researchers to andfrom Russia. Our substantive results reveal new aspects of internationalmobility in academia and its impact on a national science system whichcould inform policy development. Methodologically, our new approachcan be adopted as a framework of analysis for studying scholarly migra-tion in other countries.

Keywords: High-skilled migration · Bibliometric data · Computationaldemography · Science of science · Scientometrics.

2 A. Subbotin et al.

1 Introduction

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 includingresearchers. Our era has witnessed a large increase in high-skilled migrationbetween countries, which poses new challenges both for researchers and policymakers. Russia is not an exception in the global international migration sys-tem: a large part of its population is actively on the move for various reasons[17]. Russia is also an attractive destination for some international migrants,especially migrants from former Soviet Union countries [9, 33]. Moreover, somemigrants may consider Russia as a transit stop for further migration to otherdeveloped countries [36]. In this study, we focus on migration of research-activespecialists in the general context of scholarly migration in Russia. Previous stud-ies suggest that Russia is both a donor country and a recipient country [13, 32]for migration in the general population. If the characteristics of migrants aretaken into account, Russia is suggested to be more of a donor country [40, 43,20], i.e. , a country on the losing side of an international exchange of highlyskilled individuals.

Most studies on this topic are limited to qualitative explanations on theemigration of specialists which often do not go beyond suggesting the neces-sity of facilitating circular migration for Russia [42, 18, 28, 20, 35, 41, 40, 31, 39].Therefore, a deeper analysis is needed to quantitatively study the internationalmovements of researchers in Russia and its consequences for different fields ofscience. The literature suggest that many scientists in disciplines of physical sci-ences such as mathematics [41, 35], physics [7, 35], and computer science [35, 4]usually leave Russia. The movers are more often from major scientific centers inMoscow, St. Petersburg, Novosibirsk, and Yekaterinburg, and come from lowerage groups [10, 16], who otherwise have the potential of contributing to the Rus-sian science system for a long time. Also from this aspect, further research isneeded to accurately quantify this phenomenon with respect to similarities anddifferences between all movers in all disciplines and the origin and destinationcountries.

Quantitative studies on international migration of researchers seem to becomplicated by a lack of reliable, relevant, and comparable statistical infor-mation. Recent studies on this topic use bibliometric data to detect migrantpopulations among researchers and obtain migration trajectories and flows forfurther analysis [27, 6, 23, 15]. This method involves tracking the internationalmovements of researchers through the changes in the affiliation addresses ontheir publications. In this study, we adopt such a method for focusing on re-searchers who have published with a Russian affiliation address at some pointin 1996-2019. We track the international movements of all such researchers toquantify the overall impact of migration on the Russian science system and ondifferent areas of research within it.

Brain Drain and Brain Gain in Russia 3

2 Materials and Methods

2.1 Scopus Bibliometric data

The availability of millions of publications in the Scopus database allows us toquantitatively evaluate the international scholarly migration in Russia with abottom-up approach by aggregating movements of each researcher who has affil-iation ties to Russia at some point according to the publications indexed in theScopus database over the 1996-2019 period. A given publication may representseveral data points each indicating an affiliation address for an author at a cer-tain publication date. The unit of data is therefore an authorship record whichis the linkage between an author affiliation and a publication. The data linkedto an authorship record provide proxies not only for the geographic locationsof researchers, but also for their research areas. Scopus annotates subject codesto all the indexed publication venues based on the disciplines they cover. Thisallows us to analyze the disciplines of internationally mobile researchers basedon the subjects of their publications. There are more than 8.2 million author-ship records in Scopus from over 584’000 individual authors who have publishedwith a Russian address at some point over the 1996-2019 period. After retrievingthis data, we focus on the scholars who also have countries of affiliation otherthan Russia in their publications. This step excludes those researchers who donot have any evidence of international mobility and authors who only have onepublication. Given that migration is a rare event and only a minority of re-searchers are internationally mobile, the subset of the data we focus on wouldbe authorship records associated with 370’000 publications from nearly 20’000internationally mobile researchers.

2.2 Methods

For analyzing scholarly migration, we borrow well known and fundamental con-cepts such as origin, destination, and migrant from migration studies and repur-pose them for usage in an academic sense. Accordingly, a country of academicorigin (destination) is the country appearing in the first (last) publication ofa researcher. To refer to a researcher who have had an international move weuse the term academic migrant or migrant for brevity. We consider an inter-national mobility event if the changes in affiliations across two different yearsare such that the mode of affiliation country changes for a researcher. In Table1, we define four categories for academic migrants based on their countries ofacademic origin and destination. Internationally mobile researchers in our anal-ysis exactly belong to one of four categories: (1) immigrants (origin: not Russia,destination: Russia), (2) emigrants (origin: Russia, destination: not Russia), (3)return migrants (origin: Russia, destination: Russia), and (4) transients (origin:not Russia, destination: not Russia).

According to All Science Classification Codes (ASJC), there are four majorfields of science. The first field is life sciences including five sub-fields: (1) agri-cultural and biological sciences (2) biochemistry, genetics and molecular biology


Table 1. The classification of academic migrants

Academic origin/destination Russia Not Russia

Russia Return migrants EmigrantsNot Russia Immigrants Transients

(3) immunology and microbiology (4) neuroscience and (5) pharmacology, toxi-cology and pharmaceutics. The second field is social sciences which includes sixsub-fields: (1) arts and humanities (2) business, management and accounting (3)decision sciences (4) economics, econometrics and finance (5) psychology and(6) other social sciences. Physical sciences is the third field including ten sub-fields: (1) chemical engineering (2) chemistry (3) computer science (4) earth andplanetary sciences (5) energy (6) engineering (7) environmental science (8) ma-terials science (9) mathematics and (10) physics and astronomy. Finally, healthsciences is the fourth field which includes five sub-fields: (1) medicine (2) nursing(3) veterinary (4) dentistry and (5) health professions.

Each publication venue in Scopus is classified by possibly multiple ASJCcodes which determine the fields and sub-fields of the topics they cover. Re-searchers can potentially be active in several disciplines. 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 NCj

(d) of researcher

j in discipline d (among a total of n disciplines) is defined and formulated in Eq.(1). In Eq. (1), sjd is the frequency of discipline d in the authorship records of

individual j. In the denominator of Eq. (1),∑n

i=1 sji is the sum of frequencies of

all subjects in the authorship records of individual j.

NCj(d) =

sjd∑ni=1 s

ji

i = 1, . . . , n ∀j ∈ {1, . . . , k} (1)

It follows from Eq. (1), that the normalized contributions of each individualover all disciplines add up to one, i.e.

∑ni=1 NCj(i) = 1. For example, consider

that the authorship records of an individual over their career are as exemplifiedin Table 2.

Table 2. An example of authorship records with multiple subjects and countries

Author ID DOI ASJC Subject Country Year

X 22222 Mathematics Russia 2012X 33333 Chemistry, Energy Russia, USA 2013X 44444 Mathematics, Chemistry USA 2015

From the digital object identifiers (DOIs) in Table 2, it is evident that theseauthorship records are associated with three distinct publications. The normal-


ized contributions of the researcher, who is identified by Author ID X, are

NCX(chemistry) =

2

5, NCX

(energy) =1

5, NCX

(mathematics) =2

5.

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 Pd, 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 Pd is decimal we use arithmetic rounding.

Pd =

k∑j=1

NCj(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 obtainP immd , P emi

d , P retd , P tra

d respectively as normalized populations of immigrants, em-igrants, return migrants, and transients in discipline d.

3 Results

In this Section, we present the main results of our analysis which focuses oninternationally mobile researchers who have had ties to Russia according to Sco-pus publications in 1996-2019. This Section is structured as follows: Subsection3.1 outlines the analysis of the geography of mobile researchers (to understandtheir similarities with respect to origin and destination countries), Subsection3.2 presents our estimates of net migration rates (to evaluate the overall levelof brain circulation in Russia), and Subsection 3.3 explores disciplines of mobileresearchers (to evaluate brain circulation for each field of science in Russia).

3.1 Origin and destination countries

Figure 1 illustrates the most common countries of academic origin for immigrants(destination for emigrants) over the 1996-2019 period. We can see in Figure 1that as origins of immigrants, the United States (US), Ukraine, and Germany arethe three most common countries respectively, while as destinations of emigrantsUS and Germany are again among the most common countries, followed by theUnited Kingdom (UK) and France, while Ukraine is ranked the fifth amongcommon destinations. Moreover, it should be noted that the scale of emigrationis more than twice the scale of immigration. In this context, US, Germany, UK,and France are more likely to be a destination than an origin with respect tothe migration patterns of immigrants and emigrants.


FIN

CHN

POL

SWE

NLD

CAN

KAZ

ITA

BLR

JPN

GBR

FRA

DEU

UKR

USA

0 200 400 600 800 1000

ori

gin

number of immigrants

(a) Origins of immigrants

BLR

AUS

KAZ

ITA

JPN

FIN

NLD

CHE

SWE

CAN

UKR

FRA

GBR

DEU

USA

0 400 1200 2000

destination

number of emigrants

(b) Destinations of emigrants

Fig. 1. Top 15 academic origin countries for immigrants (a) and top 15 academicdestination countries for emigrants (b)

An exception among the top countries of origins is Ukraine with fewer emi-grants from Russia than immigrants to Russia. The number of immigrants fromUkraine to Russia is 2.5 times more than the number of emigrants from Rus-sia to Ukraine. So, Ukraine is more likely to be an origin than a destinationfrom the perspective of the Russian science system. In the general population,the Russo-Ukrainian migration relationship has been substantial, and for a longtime Russia has been the major destination for the Ukrainian migrants [11, 30].Also from April 2014 to February 2016 more than one million people migratedfrom Ukraine to Russia following the 2014 events in Ukraine [28].

The ranking for countries of academic destination indicates that Canada,Finland, Sweden, Netherlands, and China are more likely to be destinations.Canada and Finland are three times more likely to be a destination country asopposed to being an origin. Also Switzerland and Australia are common aca-demic destination countries without being among the top 15 origin countries.Poland has almost an equal number of emigrants and immigrants which makesit equally likely to be an origin and a destination. Belarus is more likely to be acountry of origin rather than a destination from the perspective of the Russianscience system.

Most countries with lower number that do not appear in Figure 1 such asIsrael, Austria, Norway, Denmark, and Spain are more likely to be countriesof academic destination than origins from a Russian perspective. Contrary tothis, Armenia and Uzbekistan are more likely to be origin countries. The out-flow from Armenia to Russia is 1.1 times more than the inflow from Russia toArmenia, for Uzbekistan this value is 1.3. The migration patterns of academicsfrom these two countries might be explained by the historical trend in patternsof general migration showing that Russia has been a primary destination for theinternational migration flows from Armenia and Uzbekistan [33, 9].


CAN

ISR

UZB

ARM

AZE

SWE

KAZ

JPN

ITA

GBR

FRA

BLR

DEU

UKR

USA

0 100 300 500

ori

gin

number of transients

(a) Origins of transients

ESP

AZE

CAN

CHE

NLD

SWE

JPN

ITA

KAZ

FRA

GBR

BLR

DEU

UKR

USA

0 100 300 500

destination

number of transients

(b) Destinations of transients

NOR

POL

CHE

FIN

NLD

KOR

SWE

CAN

ITA

UKR

JPN

GBR

FRA

DEU

USA

0 200 600 1000

inte

rmedia

te

number of return migrants

(c) Intermediate countries ofreturn migrants

Fig. 2. Top 15 origin (a) and destination (b) countries for transients, and top 15 inter-mediate countries (c) for return migrants

Subfigures 2a–2b shows the common origins and destinations for transientscholars (academic origin and destination both being countries other than Rus-sia). According to Subfigures 2a–2b, in general, the rankings of origin and des-tination countries for transients match with minor differences. The number ofoutgoing transients from Russia to the US, Netherlands, Switzerland, Germany,China, and Spain are higher than the respective number of incoming transientsfrom these countries to Russia. The outgoing transients from Russia to countriessuch as Netherlands and Switzerland are around 35% and 25% more than therespective incoming transients from these countries respectively. However, thesituation for countries such as Italy and Japan is reversed: the outgoing tran-sients from Russia are about 30% and 25% lower than the incoming transientsfrom these countries respectively. As for Belarus and Ukraine, the outflows oftransients from Russia are about 7 percent lower than the inflows of transientsto Russia from Belarus and Ukraine.

As for return migrants (both academic origin and destination being Russia),we look at the intermediate country(ies) that a given return migrant has beenaffiliated with while being temporarily away from Russia. For cases of multipleintermediate countries, we use equal weights (adding up to one for each returnmigrant) when incorporating such cases in the counts of intermidate countries.Subfigure 2c shows the most common intermediate countries for return migrants.We can see that US, Germany, France, and UK are the four most common inter-mediate countries for return migrants. It should be noted that the top countriesin this ranking mostly coincide with destinations of emigrants in Subfigure 1b,but the ranks of France and UK are swapped, and Japan and Italy have a higherrank as intermediate countries for return migrants compared to destinations ofemigrants.


3.2 Net migration rates

The absolute number of migrants and the net migration rates are commonlyused measures of the difference between movements into and out of a certainarea [26]. Net migration rate for a given area refers to the difference between in-migration and out-migration rates per 1000 people. A positive value means morepeople entering than leaving a given area. If Iy and Ey represent the number ofscholars who have moved to Russia and out of Russia respectively during yeary, and My is Russia’s population of scholars at the beginning of year y, thenthe net migration rate NMRy is calculated according to Eq. 3. In-migrationand out-migration rates can be computed from Iy/My and Ey/My respectivelywhich only take one direction of the flows into consideration.

NMRy =Iy − Ey

My× 1000 (3)

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

−6

−5

−4

−3

−2

−1

0

1

(a) Net migration rate per 1000 researchers

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

0

2

4

6

8

10

12

14

4.4 4.6 5.2 6

.4 6.8 7.1

6.2

5.1

4.7

4.4

4.5

4.3 4

3.4

3

3.1

2.9 3.5

2.9

2.2

1.6

(b) In-migration rate

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

0

2

4

6

8

10

12

14

6.5

8.5

9.9

11

.8

10

.9

8.7

7.4

6.5 7.1

5.9 6.4

5.5

4.5

4.4

3.6

3.1

3.1

2.6

2.2

2.2

1.8

(c) Out-migration rate

Fig. 3. Net migration (a), in-migration (b) and out-migration (c) rates per 1000 pop-ulation of researchers in Russia over the 1998-2018 period

In Eq. 3, the denominator, My, is obtained from the original superset ofour bibliometric data. It estimates the total number of researchers in Russia(internationally mobile or otherwise) in year y based on the affiliation addressesassociated with publication dates within a two year vicinity of year y. As we donot have data on location of researchers at a particular time within a year, weassume that the researchers with Russian addresses have been in the country two


years before and two years after the publication year unless there are evidenceto the contrary (publications showing other countries for the researcher).

Subfigure 3a 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−5.4 per 1000 researchers. Looking at in-migration rate in Subfigure 3b andout-migration rate in Subfigure 3c, one may see that for every 1000 researchersin Russia at the beginning of year 2001, 6.4 researchers have come to Russia, and11.8 researchers have left Russia resulting in a negative net flow of 5.4 per 1000researchers from Russia to the outside of Russia by the end of year 2001. Sub-figure 3a shows that over time, the net migration rate has generally increased.The highest rate is +0.9 which is for year 2015. Starting from 2015 we can see aslow downward trend in the net migration rates which ends with the value −0.2for year 2018.

3.3 Subject categories

We analyze subject categories of publications venues where different types ofmigrant scholars have published to find the the impact of migrations on differ-ent disciplines. We develop a measure inspired by net migration rate for eachdiscipline to find disciplines in which Russia is impacted the most from an im-balance of incoming and outgoing flows. To operationalize this idea, we startwith the concepts of normalized contribution and normalized count formulatedin Eqs. (1) and (2) discussed in Section 2. We evaluate the possible losses (gains)in each field by looking at the relative difference between the normalized countsof immigrants, return migrants, emigrants, and transients using Field-based NetBrain Gain (FNBGd) formulated in Eq. 4. Immigrants and return migrants af-fect the net gain of a national science system in a positive way and therefore theyhave positive coefficients in Eq. 4. In contrast, we consider negative coefficientsfor emigrants and transients in Eq. 4 because these groups of migrants affectthe net gain of a national science system in a negative way. A positive value ofFNBGd means a positive impact in terms of brain gain in discipline d. Eachterm of Eq. 4 represents the impact of the respective group of migrants.

FNBGd =P immd

Pd+

P retd

Pd− P emi

d

Pd− P tra

d

Pd(4)

To illustrate the application of our proposed measure of field-based net braingain, we use the discipline of computer science as an example. We obtain con-tributions of mobile researchers to different fields using Eq. (2) and sum up thenormalized contributions in the field of computer science for all four types ofmigrants (calculating P imm

d , P emid , P ret

d and P trad ) and all migrants together (cal-

culating Pd for d = computer science) using Eq. 2. Accordingly, the normalizedcount of mobile researchers in computer science would be Pd = 1673. Amongthis normalized population, there are 373 immigrants, 343 return migrants, 749emigrants, and 208 transients (see Table 5). Then, we use the formula in Eq. 4to calculate FNBG for computer science which is equal to −0.144. This value


means the overall migration of researchers in Russia over the period 1996-2019has led to a 14.4% net loss in the field of computer science for Russia. Figure 4shows the four terms of FNBG and its total value for all disciplines.

FNBG_d FNBG_d (total)

−0.5

−0.4

−0.3

−0.2

−0.1 0.

00.

10.

20.

30.

40.

5−0

.5−0

.4−0

.3−0

.2−0

.1 0.0

0.1

0.2

0.3

0.4

0.5

Pharmacology, Toxicology and PharmaceuticsAgricultural and Biological Sciences

Environmental ScienceEnergy

Health ProfessionsComputer ScienceDecision Sciences

Economics, Econometrics and FinanceArts and Humanities

Earth and Planetary SciencesPhysics and Astronomy

Social SciencesChemical Engineering

Business, Management and AccountingEngineering

NeuroscienceImmunology and Microbiology

VeterinaryMedicine

Biochemistry, Genetics and Molecular BiologyMaterials Science

MathematicsNursing

ChemistryPsychology

Dentistry

Terms of FNBG pertaining to

dis

cip

line

Emigrants Immigrants Return migrants Transients Total value of FNBG

Fig. 4. Field-based net brain gain for different categories of migrants

Interestingly, values of FNBG in Figure 4 show a positive net gain for den-tistry (12.3%), psychology (7.9%), and chemistry (2.9%). In nursing (-1.0%),there is a balance circulation of flows almost cancelling each other. We can alsosee that Russia has suffered a large loss in disciplines such as pharmacology,toxicology and pharmaceutics (-24.5%), agricultural and biological sciences (-18.7%), environmental science (-17.0%), and energy (-16.5%). For all the otherdisciplines, FNBG values show a loss, but to a smaller degree.

The results indicate that there is heterogeneity in the impact of interna-tional mobility of researchers on the Russian science system. This observationcasts doubt on viewing a national science system as just one unit with a simplepositive/negative response to international mobility, instead, the components ofsuch a system could be differently impacted by the balance of migration flowsor lack thereof. Note that, if we only consider terms of FNBG pertaining to im-migrants and emigrants, the left panel of Figure 4 shows Russia suffering a netloss in all disciplines because emigrants outnumber immigrants. However, returnmigrants and transients account for a considerable proportions of migrations andtherefore totally excluding them from the analysis could be contestable.

4 Limitations

A major limitation of this study, as well as a remarkable merit, is the use ofbibliometric data, and the unique view they provide. The time which is re-quired to conduct and publish research is an important factor to keep in mind


when interpreting the time component of results on mobility patterns observedthrough bibliometric data. In some cases, it may take years from the initiationof a research project to the publication [12], and such a time lag prevents usfrom observing the movements of researchers with an ideal temporal accuracy.Also in the literature, it is noted that a one-time usage of an affiliation is notguaranteed to show a direct attachment to the country of affiliation [24]. Ourconservative approach helps with this concern by detecting international movesonly if the mode of country of affiliation is changed across different years. More-over, we cannot observe and track any migration events that are not representedin publications indexed in Scopus. Scopus database could be biased, and therecould be an over- or an under-representation of some countries, scientific fields,and languages [29, 38]. Also, given the fact that we are investigating a specificperiod of time, our data suffers from left-truncation.

Despite these limitations, bibliometric data allow a study on migration ofresearchers cross the disciplinary boundaries and be more contemporary andextensive compared to what traditional data sources may allow [1]. This studymakes several contributions to the literature: both methodological contributionsin usage of bibliometric data and substantive contributions to the study of high-skilled migration in Russia. A missing piece of puzzle for understanding academicbrain circulation has been key migration statistics which our study provides fora commonly debated country of brain drain. Our contributions are based onlarge-scale and detailed digitized records which allow one to take a first step inquantifying this social phenomenon.

5 Discussion and Summary

In this study, we used affiliation addresses from millions of publications to presenta comprehensive and detailed picture of academic migration in Russia. Our goalwas to understand the patterns of scholarly migration by tracking the interna-tional movements of researchers and to identify the impact of such movementson the Russian science system overall and in each field of scholarship. The use oflarge-scale bibliometric data from Scopus over 1996-2019 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 analy-sis such as gender and academic age as well as measures for quality and quantityof research output for migrating researchers.

Our analysis of four main categories of academic migrants (immigrants, em-igrants, return migrants, and transients) revealed the similarities between theircommon countries of academic origin and destination while contrasting theirdifferences in migration patterns and the impact on the Russian science system.The United States and Germany are the largest scientific hubs linked to Russia.Ukraine turned out to be one of the main donors of researchers to Russia, whichcould be partly explained by the patterns of Russo-Ukrainian migration in the


general population. Using net migration rate, it was shown that while in the late1990s and early 2000s Russia has been overall on the losing side of a brain circu-lation system, this has not been the case in recent years when net migration ratehas been even positive for some years and generally oscillates around zero indi-cating a balance between the annual incoming and outgoing flows of researchers.Our numerical results indicate that the overall lack of balance between incomingand outgoing flows of researchers has improved for Russia, but it could be stilltoo early to call 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 gain for quantifying the impact of inter-national migration on each specific field of scholarship. The resulted analysisshowed that over the time period 1996-2019, there is a relatively large outflow ofspecialists in most fields of scholarship. Our results indicate that the number ofresearchers coming to Russia in fields such as mathematics, biochemistry, genet-ics, and molecular biology is close to the number of those who leave Russia whileother studies suggest that specialists in these fields are actively leaving Russia,placing these areas on a par with fields like computer science and physics [36,35] for which we see distinctive patterns of brain circulation.

Our analysis allows us to identify four clusters for 26 fields of scholarshipbased on their FNBG values. The first cluster is associated with a large negativevalue for net brain gain. It includes 13 fields in which Russian science systemseems to have suffered a great loss as FNBG values are above 10%. The secondcluster also contains fields associated with negative values for FNBG, but thevalues are smaller in absolute terms. For 9 disciplines, Russia’s net brain gain isnegative, but less than 10%. The third cluster only contains nursing for whichRussia has a balanced circulation of brain as measured by an FNBG close tozero. The most striking observation in our results was identifying a fourth clustercontaining three disciplines in which Russia has been overall on the winningside of a brain circulation system. The patterns for dentistry, psychology, andchemistry is in contrast to Russia implicitly assumed in the literature to be losingscientists [41, 7, 35, 4].

The results that this research substantiated for the first time are generaliz-able within the limitations of what bibliometric data offer which we discussed inSection 4. Keeping the possible caveats in mind, our substantive and methodolog-ical contributions can be used in furthering our understanding of highly-skilledmigration in academia.For the specific case of this study, timely and relevantstatistics were required. Our findings revealed new insights for Russia which ishoped to be used in policy development involving highly qualified professionals.The methodological contribution of this study can be applied to other countriesas a framework of analysis to examine other national science systems and theimpact of international mobility of researchers on them. This study has onlyscratched the surface on the study of migration among researchers and a newapplication of bibliometric data while many questions are yet left unansweredfor which we hope to have provided an initial point of direction.


Acknowledgements

Authors highly appreciate the technical support from Tom Theile and discus-sions with Emilio Zagheni which helped improving this study. This study hasreceived access to the bibliometric data through the project “KompetenzzentrumBibliometrie” and the authors acknowledge their funder Bundesministerium furBildung und Forschung (funding identification number 01PQ17001).

Appendix

Background on high-skilled and academic migration in Russia

Over half a century ago, it was predicted that migration of highly skilled special-ists would become extremely crucial [37]. It is decades now that this predictionhas turned out to be right in several contexts including academia. In the aca-demic job market, institutions compete for attracting highly skilled researchersdespite the general growth of higher education in the past few decades.

In 1963, the United Kingdom Royal Society published a report on the sub-stantial increase in emigration of British scientists and engineers from the UnitedKingdom. The report attracted mass media attention and the Evening Standardnewspaper coined the term “brain drain” [14] which refers to the emigration ofhighly trained or qualified people from a particular country. While brain drainis still a common topic of heated debates, other terms like “brain exchange” or“brain circulation” allow a more comprehensive view on international mobilityof the highly skilled as a social phenomenon and therefore seem to be moreappropriate for studying international migration in academia.

Some studies involve the history and description of high-skilled migrationas a whole [39, 22]. The historical roots of the high-skilled migration in Russiashow a drastically different picture for the late 17th century and 18th centurywhen the Russian Empire attracted many competent high-skilled professionalsfrom abroad [8, 34]. This was due to the need to put the country on a parwith European states in terms of scientific and technical development, as well asestablishing St. Petersburg as a modern city. Having said that, the contemporaryphenomenon of high-skilled migration in Russia is more often traced back to thecollapse of the USSR when many highly qualified specialists left the country. Thisexplains why most brain drain debates in Russia started in the 1990s. However,the scale of brain drain in Russia is said to have increased over the past fewyears [17, 5].

Previous studies on this topic discuss reasons and consequences of the em-igration of highly skilled specialists from Russia [2, 18, 19, 25, 36, 39, 40]. Somecommon reasons for the departure of specialists from Russia include: differencesin the level of income between Russia and destination countries [40, 25, 2], thedecline in prestige of scientific and academic jobs in Russia [40, 2, 19], the inabil-ity of Russia-based researchers to realize their full scientific potentials [2, 19],the lack of research infrastructure, facilities and conditions required for conduct-ing research in Russia [40, 25, 2, 19], and the reduction of state expenditures onscience and education [39, 19].


Given these reasons, one may expect that some migrant scholars may con-tinue scientific cooperation with the country of departure, but this is not common[3]. Different approaches to the factor of time of stay and categories of migrantsmake it difficult to compare the results of statistical observation by countries(from a spatial aspect) and in their dynamics inside one region (from a temporalaspect). Some migrant researchers do not stay in the destination country per-manently [10, 18]. Yet, brain drain could have several negative consequences forthe origin countries such as lack of collaboration between the origin and destina-tion countries and institutions [7]. Also, the relocation of families of specialistscreates a vicious cycle reinforcing more brain drain in many cases [10]. This alsocould lead to emergence of national scientific diaspora in the destination coun-tries [21] which could ultimately lead to an origin country’s lag in science andtechnology. The weakening of a country’s scientific potential and the slowdownof research and development activities and innovation, would in turn lead to aloss of return on investments and potential revenue (in terms of education costsand the expected contribution to the science and economy of the origin coun-try) [40, 25]. Also, years of generational efforts to create a dynasty of specialiststhrough mentorship and supervision involved in academic work could get sub-stantial harm as the emigration of the senior researchers slows down the processfor social reproduction of intellectuals [40, 36, 18].

Additional results

The most common ASJC codes in the authorship records of emigrants and im-migrants are social sciences, arts and humanities, and medicine, engineering,and computer science. However, there is heterogeneity in publications of dif-ferent disciplines in terms of the number of publication venues and publishingfrequency for researchers in different fields. Therefore, a simple comparison offrequencies would not be useful especially for our purposes of quantifying theimpact of migration.

VeterinaryDecision SciencesHealth Professions

DentistryNeuroscience

EnergyNursing

Chemical EngineeringImmunology and Microbiology

Pharmacology, Toxicology and PharmaceuticsChemistry

Materials SciencePhysics and Astronomy

MathematicsEarth and Planetary Sciences

Agricultural and Biological SciencesPsychology

Environmental ScienceBiochemistry, Genetics and Molecular Biology

Economics, Econometrics and FinanceBusiness, Management and Accounting

Computer ScienceEngineering

MedicineArts and Humanities

Social Sciences

1e+0

0

1e+0

1

1e+0

2

1e+0

3

1e+0

4

1e+0

5

1e+0

6

1e+0

7

1e+0

8

1e+0

9

1e+1

0

1e+1

1

1e+1

2

subje

ct

Migrants Emigrants Immigrants Return migrants Transients

Fig. 5. The frequencies of disciplines in authorship records of each type of migrants.


Figure 5 illustrates the frequencies of different disciplines among migrantresearchers of different types. Note that the x-axis in on a logarithmic scale.The order-of-magnitude difference between the frequencies of disciplines amongpublications of researchers shows that a naive comparison of these frequencieswould not be particularly informative for understanding the impact of interna-tional migration on each discipline. This motivates the normalization methodswe have proposed and implemented in this study.

Table 5 provides values of P immd , P emi

d , P retd and P tra

d for all disciplines.

Table 3. The normalized counts of migrants in each of the 26 ASJC disciplines

Discipline Immigrants Emigrants Return migrants Transients

dentistry 20 24 21 8psychology 151 175 109 47chemistry 60 75 46 25nursing 31 42 19 9mathematics 98 180 111 52materials science 85 130 70 43biochemistry, genetics, molec. 155 235 112 64medicine 697 1196 607 310veterinary 10 17 8 4immunology and microbiology 29 49 26 16neuroscience 22 39 19 10engineering 488 835 420 257business, management, acc. 241 424 211 123chemical engineering 29 50 25 16social sciences 949 1720 837 502physics and astronomy 88 167 86 54earth and planetary sciences 104 195 91 54arts and humanities 659 1298 590 332economics, econometrics, fin. 197 383 176 107decision sciences 10 21 11 7computer science 373 749 343 208health professions 12 24 11 7energy 22 40 16 13environmental science 112 234 95 58agricultural and biological sci. 99 196 83 70pharmacology, tox., pharmac. 30 77 27 17

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