Provision and analysis of key indicators in research and innovation. Policy brief F, Scientific mobility.
Date: 2021-10-11
Institutions: Directorate-General for Research and Innovation
Subjects: EU research policy, innovation, job mobility, performance measurement, policymaking, report, scientific profession

Provision and analysis of key 
indicators in research and 
innovation 

Policy brief F – Scientific mobility

Written by Guillaume Roberge and David Campbell 
March 2021 

 
 
 
 
 
 
 
 
 
 
Provision  and  analysis  of  key  indicators  in  research  and  innovation.  Policy  brief  F  –  Scientific 
mobility. 

European Commission 
Directorate-General for Research and Innovation 
Directorate G — Common Policy Centre 
Unit G.1 — Common R&I Strategy & Foresight Service 
Contact   Athina Karvounaraki 

Email  

Tiago Pereira 
Alexandr Hobza 
RTD-STATISTICS@ec.europa.eu 
Athina.KARVOUNARAKI@ec.europa.eu 
Tiago.PEREIRA1@ec.europa.eu   
RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

Manuscript completed in March 2021. 

This document has been prepared for the European Commission, however it reflects the views only of the authors, and the 
European Commission is not liable for any consequence stemming from the reuse of this publication. 

More information on the European Union is available on the internet (http://europa.eu). 

PDF  

ISBN 978-92-76-41092-8 

doi:10.2777/073061 

KI-09-21-364-EN-N 

Luxembourg: Publications Office of the European Union, 2021 

© European Union, 2021 

The  reuse  policy  of  European  Commission  documents  is  implemented  based  on  Commission  Decision  2011/833/EU  of  12 
December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of 
this  document 
licence 
(https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any  
changes are indicated. 

is  authorised  under  a  Creative  Commons  Attribution  4.0 

(CC-BY  4.0) 

International 

For any use or reproduction of elements that are not owned by the European Union, permission may need to be sought directly 
from the respective rightholders. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Provision and analysis of key 
indicators in research and 
innovation 

Policy brief F – Scientific mobility 

Prepared by 

2021  

Directorate-General for Research and Innovation 

EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 
1  Introduction ............................................................................................................... 8 
2  Methods ..................................................................................................................... 10 
3  Results & discussion .................................................................................................... 15 
16 
27 
37 
41 
4  Conclusion ................................................................................................................. 45 

3.1  General concepts on mobility and mobility in the training stage 
3.2  Brain gains amongst populations of researchers: winners and losers 
3.3 
3.4  Mobility of researchers by scientific disciplines 

Impact of mobility on scientific production and citation impact metrics 

Table 1 

Share  of  researchers  never  experiencing  international  mobility  during  their  publishing 
career............................................................................................................17 

Table 2  Mobility counts of researchers experiencing mobility events only in the first five years of 

Table 3 

Table 4 

Table 5 

their publishing career .....................................................................................20 
Number and percentage of researchers  having  more than a single country affiliation  in 
their first publishing year .................................................................................23 
Factors acting as barriers to mobility as reported by post-PhD mobile researchers in the 
MORE3 survey ................................................................................................26 
Factors acting as barriers to mobility as reported by post-PhD non-mobile researchers in 
the MORE3 survey ...........................................................................................26 
Table 6  Gained and lost researchers per country ............................................................29 
Table 7 

Average position of countries per EU-27 region, in terms of net gains of individuals and 
percentages against maximum population size ...................................................30 
Intra-, inter- and external EU-27 mobility balances per European region ................31 
Top mobility destinations of EU-27 countries, globally and within the EU-27 ...........34 

Table 8 
Table 9 
Table 10  Average  ranking  of  EU-27  countries  as  mobility  destinations,  for  researchers  of  other 

EU-27 countries, and globally ...........................................................................35 

Table 11  Average ranking of EU-27 regions as migratory destinations of researchers for other EU-

27 regions ......................................................................................................36 
Table 12  Average ranking of EU regions as mobility destinations of EU-27 nations ...............37 
Table 13  Productivity  and  citation  impact  of  mobile  versus  non-mobile  researchers,  per  country

  39 

Table 14  Productivity  and  citation  impact  of  mobile  versus  non-mobile  researchers,  average  per 

European regions ............................................................................................40 

Table 15  Difference in impact metrics for mobile researchers, before and after the first mobility to 

a new country occurring after the first 5 years of a publishing career ....................41 

Table 16  Productivity  and  citation  impact  of  mobile  versus  non-mobile  researchers,  per  field  of 

science ..........................................................................................................42 

Figure 1  Percentage of non-mobile researchers per EU-27 region ......................................18 
Figure 2  Percentage  of  researchers  affiliated  to  more  than  one  country  in  their  first  publication 

year, by EU-27 regions ....................................................................................24 

Figure 3  Correlation between MORE3 survey data and international mobility measured in Scopus

  25 

Figure 4  Average  ranking  of  scientific  fields  according  to  shares  of  mobile  researchers,  per 

scientific domain .............................................................................................43 

Figure 5  Difference  in  impact  metrics  (scores  and  rankings)  between  mobile  and  non-mobile 

cohorts, per scientific domain after aggregation of data per scientific field ..............44 

Figure 6  Correlation  between  international  collaboration  rates  and  differences  in  ARC  scores 

between mobile and non-mobile cohorts, per field of science ................................45 

2 

 
 
 
 
 
 
EXECUTIVE SUMMARY 

This  policy  brief  presents  many  findings  to  the  reader,  most  of  them  responding  to 
questions  raised  by  representatives  of  the  European  Commission.  This  executive 
summary focuses on these questions, in addition to other findings that were identified as 
having  policy  implications.  These  findings  are  easily  accessible  throughout  the  report  in 
text boxes1. Some of the questions that could be addressed within the parameters of the 
work performed for this policy brief are as follows: 

  Is  there  a  tangible  difference  between  the  scientific  output  and  impact  of  mobile 

researchers compared to static researchers? 

‒  Data on mobile and non-mobile cohorts illustrate that mobile researchers appear to 
present  with  higher  scientific  productivity  (i.e.  more  papers  published)  and  higher 
scientific  impact  (i.e.  more  citations  received  and  publishing  in  higher  impact 
journals)  compared  to  their  static  colleagues.  These  findings  hold  true  for  most 
countries and across all fields of science, at varying levels. 

  Is  there  a  tangible  change  in  a  mobile  researcher’s  scientific  output  and  impact  as 

he/she is migrating? 

‒  Although it is difficult to infer a causal relationship between mobility and changes in 
scientific  output/impact  based  on  the  analyses  presented  in  this  brief,  data 
prepared  before  and  after  mobility  do  point  towards  increases  in  impact  metrics 
after  the  first  mobility  event  (i.e.  occurring  in  the  first  five  years  of  a  publishing 
career  assumed  to  correspond  to  the  training  period  of  researchers).  However, 
without  the  use  of  a  control  group,  the  setup  of  which  was  beyond  scope  for  this 
policy brief, it is not possible to unambiguously attribute, even partly, the observed 
changes  to  mobility.  Other  factors  such  as  normal  career  progression  could  also 
explain these changes. 

  Which countries benefit from a net input of foreign researchers, and at the expense of 

which countries? 

‒  Countries  having  the  highest  net  balance  of  foreign  researchers  (with  top  five 
sources)  include  the  United  States  (UK,  Canada,  China,  Germany,  Japan),  China 
(US,  Japan,  UK,  Canada,  Germany),  Australia  (US,  UK,  Canada,  New  Zealand, 
Germany), Switzerland (US, Germany, France, UK, Italy), Canada (US, UK, France, 
Australia,  Germany),  the  United  Kingdom  (US,  Germany,  Australia,  France, 
Canada),  South  Korea  (US,  Japan,  India,  China,  UK),  Spain  (US,  UK,  France, 
Germany,  Italy),  Norway  (US,  UK,  Sweden,  Germany,  France)  and  Brazil  (US, 
Portugal, France, UK, Spain). 

  Which  countries  experience  a  net  loss  of  researchers,  and  to  the  benefit  of  which 

countries? 

‒  The  most  notable  countries  experiencing  a  net  loss  of  researchers  (with  top  five 
destinations) include Belarus (Russia, Germany, US, Poland, UK), Japan (US, China, 
UK, South Korea, Germany), Ukraine (Russia, US, UK, Germany, Poland), Slovakia 
(Czech  Republic,  US,  UK,  Germany,  Slovenia),  Hungary  (US,  Germany,  UK, 
Switzerland,  Italy),  Armenia  (US,  Russia,  Germany,  Italy,  France),  Israel  (US,  UK, 
Canada, Germany, France) and Italy (US, UK, France, Germany, Switzerland). 

1 For a more comprehensive overview of all the study findings, readers can refer to the abstract. 

3 

 
                                                 
The  findings  reported  as  having  policy  implications  are  grouped  below  by  relevance  to 
regions, career stage and other findings. 

Regional findings: 

  Northern  and  western  Europe  are  the  main  attractors  in  the  EU-27,  considering  all 
countries,  in  and  outside  of  the  EU-27,  as  potential  donors.  The  pattern  remains  the 
same  when  focusing  on  mobility  amongst  EU-27  Member  States,  amongst  EU-27 
regions or exclusively with non-EU-27 countries. These regional preferences were also 
generally  reflected  at  the  level  of  individual  Member  States.  Northern  and  western 
European Member States ranked highest in terms of mobility destinations (accounting 
for  sources  within  and  beyond  the  EU-27),  while  eastern  European  nations  nearly 
always ranked lowest.  Although no major  ‘brain drains’ were detected within the  EU-
27,  the  above  finding  should  be  given  due  consideration  in  drafting/implementing 
future  policies  towards  achieving  the  European  Research  Area’s  third  priority  on  an 
open labour market for researchers. 

  From  the  perspective  of  drafting/implementing  future  policies  on  mobility,  it  also 
appears  relevant  to  further  investigate  these  regional  patterns  to  better  understand 
the  dynamics  at  play.  For  example,  researchers  from  eastern  European  nations  (and 
to  a  milder  degree  southern  European  nations)  were,  relative  to  countries  in  other 
European  regions,  more  likely  to  report  professional  and  administrative  factors  as 
barriers  to  mobility,  and  this  from  both  mobile  and  non-mobile  cohorts  (MORE3 
survey).  Further  investigations  of  the  factors  underlying  these  differences  (e.g. 
through surveys) could guide the design and implementation of specific interventions 
aimed  at  levelling  the  playing  field  for  researcher  mobility  across  all  of  Europe.  For 
example,  new  or  updated  policies  that  best  suit  the  needs  of  the  least  mobile 
populations  of  researchers  (eastern  and  southern  Europe)  could  help  alleviate  these 
barriers to ensure that EU-27 researchers have, in as much as is feasible, equal access 
to mobility, be it on a factual or perceived basis. 

  If effective, such policies should bring positive impacts to all of Europe, as results from 
this policy brief show that mobility brings positive spillover effects (especially in terms 
of  citation  impact)  to  all  nations  involved.  In  particular,  eastern  European  nations, 
which  as  discussed  earlier  had  some  of  the  lowest  international  mobility  balances  in 
the  European  Union,  appear  to  be  those  who  benefited  most,  along  with  southern 
European  countries,  from  international  mobility  in  terms  of  scientific  impact  when 
comparing their mobile and non-mobile researcher populations. This finding is a strong 
argument  in  favour  of  policies  to  increase  the  migratory  behaviours  to  and  from 
eastern  European  countries,  in  the  latter  case  through  mechanisms  fostering  the 
return  of  researchers  to  their  home  country.  This  study’s  results  suggest  that  such 
policies could further improve the scientific impact of eastern Europe, which came last 
for its scientific impact among all four European regions. At the same time, this could 
ensure  a  more  balanced  playing  field  across  Europe–  for  instance,  in  competitive 
grants  processes.  In  turn,  Europe’s  overall  scientific  competitiveness  could  be 
improved. 

Career stage findings: 

  Training programmes in cotutelle could be targeted as a  mechanism to help increase 
mobility  for  countries  where  the  researcher  population  is  less  mobile.  This  study’s 
results  suggest  there  is  a  positive  correlation  between  multi-country  co-affiliations  in 
the  training  period  and  subsequent  mobility,  when  looking  at  results  across  highly 
mobile and highly non-mobile country populations. 

  Individuals  who  are  mobile  during  the  early  training  years  are  much  more  likely  to 
stay  in  research  compared  to  those  who  do  not  move.  Policies  fostering  greater 
mobility in this critical phase of a researcher’s career (e.g. through cotutelles) could be 
considered  to  increase  the  retention  of  graduate/post-graduate  students  in  research. 

4 

 
This  would  be  especially  relevant  for  countries  and  scientific  areas  where  shortages 
and/or retention issues in the pool of available researchers are identified. 

Other findings: 

  The  United  Kingdom  has  been  an  important  actor  in  the  mobility  network  of  Europe. 
Post-Brexit,  new  barriers  for  mobility  between  the  United  Kingdom  and  EU  Member 
States  could  substantially  alter  pre-existing  dynamics.  The  importance  of  the  United 
Kingdom  to  the  European  network  should  thus  be  accounted  for  in  drafting  future 
mobility policies for the EU within and beyond its borders. 

  Although  mobility  appears  to  have  a  positive  effect  on  all  impact  indicators  and 
productivity  metrics,  the  largest  effects  are  measured  for  the  most  cited  publications 
(e.g.  the  top  1 %  most  cited  papers).  These  publications  often  being associated  with 
research excellence, this finding could be quite critical for the decision-making process 
of EU programmes aimed at research excellence (e.g. ERC programmes). 

5 

 
 
 
ABSTRACT 

Research  mobility  has  been  on  the  rise  for  several  decades  and  is  a  critical  part  of  the 
knowledge  and  innovation  transfer  process.  In  a  global  economy,  countries  are 
competing  not  only  to  keep  their  trained  workers  but  also  to  attract  foreign  skilled 
workers  to  fill  critical  positions  in  their  national  market.  In  this  policy  brief,  countries 
acting as magnets and those experiencing brain drains were identified within and beyond 
Europe. The findings demonstrate that although there do not appear to be massive brain 
drains in the European community, there are nevertheless notable regional patterns, with 
western  and  northern  European  nations  having  higher  net  balances  of  researchers  than 
their eastern and southern  counterparts. The most notable attractors at the world level, 
in  absolute  terms,  include  the  United  States,  the  United  Kingdom,  Germany,  Canada, 
China, Australia and Switzerland. 

While  international  mobility  is  frequent,  it  is  not  the  dominant  career  path,  with  only 
about  a  third  of  all  researchers  who  are  still  active  15  years  after  their  first  publication 
having  switched  country  at  least  once  during  this  time  frame.  Again,  a  clear  pattern  is 
observable  in  Europe,  with  eastern  European  researchers  much  more  likely  never  to 
migrate compared to researchers from other parts of Europe, joining other low migratory 
nations  such  as  Turkey,  China,  Russia,  Japan  and  Brazil.  Within  the  EU-27,  the  main 
attractors  are  mostly  located  in  western  and  northern  Europe,  these  regions  ranking  as 
the top destinations for most countries, with southern Europe coming in third and eastern 
Europe ranking last of the four regions defined for the purposes of this policy brief. While 
the  United  States  and  the  United  Kingdom  are  the  top  two  researcher  destinations  for 
most EU-27 countries, of the EU-27 destinations, Germany ranks first by a large margin, 
followed  by  France  and  Italy,  then  Spain,  Sweden  and  the  Netherlands.  The  least 
frequent  destinations  included  Malta,  Latvia,  Cyprus,  Luxembourg,  Estonia,  Croatia, 
Lithuania,  Bulgaria  and  Slovenia.  Of  course,  mobility  attraction  is  somewhat  limited  by 
the capacity of countries to welcome researchers, which itself is linked to the number of 
job positions available in a  country.  Therefore, it is not surprising to  see  countries such 
as  Malta,  Cyprus  and  Luxembourg  at  the  bottom  of  the  ranking,  each  having  a  limited 
number of universities2 that can act as host institutions for researchers who would want 
to migrate. 

The findings presented in this policy brief are quite relevant at a time when  the mobility 
of  people  has  never  been  a  more  prominent  factor,  pre-  and  post-COVID-19. 
Unsurprisingly,  the  most  attractive  countries  in  terms  of  migration  –  the  United  States 
and  the  United  Kingdom  –  host  some  of  the  most  prestigious  universities  in  the  world, 
which act as important drawcards for these countries. Given its population, Canada  also 
performs  quite  well,  attracting  a  similar  number  of  researchers  to  those  of  more 
populated  countries  such  as  Germany  and  France.  China,  with  its  high  number  of 
researchers, ranks relatively low as a destination; however, when its inflow is  combined 
with  its  low  number  of  departing  researchers,  the  country  ends  up  with  the  second 
largest net migratory balance (in absolute terms), just shy of the US migratory balance. 

A  more  in-depth  analysis  of  mobility  between  country  pairs  within  the  EU-27 
demonstrated that although there do not seem to be  large-scale brain drains within the 
community,  a  pattern  favouring  northern  and  western  nations  appears  to  be  present, 
with eastern and southern European nations presenting weaker migratory balances with 
other European  regions, while  northern and western  Europe present  with slightly higher 

2  While  research  can  be  led  outside  academia,  in  most  countries,  research  positions  are  mostly 

found within universities. 

6 

 
                                                 
regional  migratory  balance.  However,  less  than  two  percentage  points  separate  all 
regions  generally,  again  a  clear  indication  that  there  are  probably  no  large-scale  brain 
drains between European regions when global researcher populations are considered. 

It was also possible to demonstrate that findings prepared with Scopus data are roughly 
in  line  with  survey  data  from  the  MORE3  survey  regarding  scientific  mobility.  This 
alignment  applied  to  statistics  on  mobility  as  well  as  rankings  of  EU-27  regions  and 
perceived  barriers  to  mobility.  This  finding  highlights  the  potential  to  use  bibliometric 
data  to  complement  survey  data,  which  are  quite  costly  and  cannot  be  updated 
frequently. 

There is strong evidence in the literature that mobility generates citations. Findings from 
this  policy  brief  strongly  support  these  conclusions,  with  almost  all  countries  presenting 
higher  impact  from  their  native  internationally  mobile  researcher  populations  compared 
to their non-mobile populations. At the level of the EU-27, mobility generated an increase 
ranging  from  20 %  to  60 %  depending  on  the  indicator,  with  the  most  important  gains 
being observed for the share of publications in the 1 % most cited, an indication that the 
mechanisms behind mobility might be quite important in the process of generating high 
levels  of  citations.  Notable  gains  between  mobile  and  non-mobile  cohorts  are  observed 
for  all  four  European  regions,  with  the  largest  increases  measured  for  southern  and 
eastern European nations, followed by western and northern European nations. The main 
hypothesis  as  to  why  western  and  northern  European  researchers  appear  to  reap  fewer 
gains  in  terms  of  citations  following  international  mobility  is  that  their  average  citation 
scores are already much higher than those of their southern and eastern European peers, 
leaving  less  room  for  increases.  Another  notable  finding  was  that  despite  increases  for 
citation  metrics  following  mobility,  mobile  eastern  European  researchers  still  performed 
below the world level on all three impact metrics selected for this brief, a reminder of the 
existing  inequalities  within  Europe  regarding  citation  behaviours.  Finally,  a  longitudinal 
analysis  of  the  performance  of  researchers  prior  to  and  after  mobility  suggests  that 
mobility does result in increased performance, both for scientific productivity and impact, 
but caution is advised as it is rather difficult to pinpoint and isolate the effect of mobility 
from other potential causes. 

When  comparing  across  fields  of  science,  the  same  finding  holds  for  all  22  fields  of 
science of the Science-Metrix classification, though to varying degrees. The field with the 
highest  share  of  mobile  researchers  is  Physics  &  Astronomy,  followed  by  Biomedical 
Research  and  Economics  &  Business.  Overall,  the  domains  with  the  greatest  mobility 
levels are Natural Sciences and Economic & Social Sciences, distantly followed by Health 
Sciences. Applied Sciences and Arts & Humanities  register the lowest  mobility levels. In 
terms of impact, mobile cohorts across all fields present higher impact metrics compared 
to their respective non-mobile cohorts for the EU-27. An inverse relationship between the 
rate  of  international  co-publications  and  the  citation  gains  of  mobile  versus  non-mobile 
cohorts  was  detected  for  the  EU-27.  One  potential  explanation  regarding  this  finding  is 
that  as  international  co-authorship  behaviours  increase  in  a  specific  discipline,  potential 
gains  in  citations  following  mobility  become  more  limited  as  the  potential  gains  from 
international interconnectivity might have already been activated. 

7 

 
 
 
1 

Introduction 

There has always been an international aspect to science; this aspect became even more 
critical  and  apparent  with  the  advent  of  the  internet,  greatly  facilitating  long-distance 
interaction  between  researchers  (Rodrigues,  Nimrichter,  &  Cordero,  2016).  According  to 
multiple  studies  (OECD,  2016),  there  is  an  increasing  trend  in  the  mobility  of  highly 
skilled workers, fostering greater circulation of  tacit knowledge and innovation. Findings 
of  these  studies  highlight  the  benefits  to  home  countries  of  returning  workers,  as  they 
bring  back  new  skills  gained  abroad.  While  international  workers  and  students  still 
represent a small fraction of national populations, migrants have been shown to exert a 
disproportionate  impact  on  the  economic,  scientific  and  innovation  activities  of  nations 
(OECD, 2016). This is particularly true for skilled Asian migrants in science, technology, 
engineering and mathematics, which is of particular interest for this study given its focus 
on scientific mobility as revealed through publication authorships. 

Within the European Research Area (ERA), mobility between countries is critical towards 
achieving  Priority  3,  on  an  open  labour  market  for  researchers  (European  Commission, 
2019). Within this context, mobility is, on the one hand, critical for countries to gain an 
economic  edge  (brain  gain  through  immigration  and  returning  workers),  while  on  the 
other hand, migration balance across European countries is essential to Europe’s overall 
competitiveness (Morano-Foadi, 2005). While the two may appear antagonistic, the latter 
is  not  necessarily  incompatible  with  the  former  if  mobility  serves  to  build  capacity  in 
areas  of  strategic  relevance  to  each  country,  assuming  countries  emphasise 
complementary  areas  such  as  those  identified  in  smart  specialisation  strategies.  The 
European  Structural  and  Investment  Funds  (ESFRI)  is  an  example  of  a  mechanism  in 
place  to  drive  capacity  building  in  areas  of  strategic  relevance  per  the  regional  smart 
specialisation strategies of countries3. 

The  above  evidence  highlights  the  importance  of  devising  means  to  properly  track 
scientific mobility generally, as well as in assessing the impacts of interventions building 
on mobility to promote knowledge transfer. Multiple methods to track mobility have been 
designed  in  recent  years,  taking  advantage  of  partially  disambiguated  author  profiles  in 
bibliographic  databases  to  follow  the  path  of  individual  researchers  through  their  career 
(Aman, 2018; Campbell et al., 2015; Moed, Aisati, & Plume, 2013; Roberge & Campbell, 
2012). More recently, new scientific mobility indicators have started to emerge as a new 
way  to  investigate  scientific  migrations  (Sugimoto,  Robinson-Garcia,  &  Costas,  2016; 
Robinson-García  et  al.,  2018).  Additional  issues  related  to  mobility  are  now  frequently 
studied, such as its impact on the citation profiles of migrating scholars (Sugimoto et al., 
2017), on the coherence/incoherence of mobility and co-authorship patterns (Chinchilla-
Rodríguez et al., 2018), and on the effects of intersectoral mobility on the employability 
of PhD students and R&D intensity of companies (Hristov, Slavcheva, Jonkers, & Szkuta, 
2016). 

This  report  presents  mobility  patterns  across  countries  and  over  time,  focusing  on  the 
EU-27 and the ERA. The analyses were produced using disambiguated author profiles in 
Scopus and are reported at the country level as well as at the aggregate levels of the EU-
27 and the ERA. For the EU-27, data are also reported by region (west, north, south and 
east).  Note  that  key  international  partners  (based  on  the  whole  of  science)  outside 
Europe (INCO–EU countries) were also considered. These analyses were produced with a 
view to inform a few policy questions pertaining to mobility: 

  Which countries benefit from a net input of foreign researchers, and at the expense of 
which  countries?  Which  countries  experience  a  net  loss  of  researchers,  and  to  the 
benefit  of  which  countries?  These  two  questions  were  informed  by  identifying  the 

3 https://www.esfri.eu/national-roadmaps  

8 

 
                                                 
countries benefiting from a net input of foreign researchers and those experiencing net 
losses. Because of limitations related to the identification of a researcher’s citizenship, 
researchers  were  assigned  their  first  affiliation  country,  per  their  first  scientific 
publication  in  Scopus,  as  their  country  of  origin.  The  donors  for  countries  with  net 
gains as well as the recipients for countries with net losses were also identified. More 
advanced mobility patterns, such as the proportion of mobile researchers returning to 
their  home  country  (or  maintaining  professional  relationships  with  it),  were  also 
analysed. Note that only researchers who pursued academic careers were considered. 

  Is  there  a  tangible  difference  between  the  scientific  output  and  impact  of  mobile 
researchers  compared  to  static  researchers?  Is  there  a  tangible  change  in  a  mobile 
researcher’s scientific output and impact as he/she is migrating? These two additional 
questions  were  addressed,  as  far  as  was  possible  within  the  scope  of  this  study,  by 
first comparing the productivity and impact of cohorts of mobile and static researchers 
at country level. Comparisons prior to and after mobility for mobile cohorts were also 
performed to detect the possible contribution of mobility to a positive effect on these 
bibliometric indicators. Finally, mobility data per field of science were also prepared to 
assess  whether  mobility  was  a  relevant  mechanism  across  all  disciplines  and  if 
mobility patterns were similar. Our results were analysed in light of another study on 
the  effects  of  mobility  for  the  productivity  and  scientific  impact  of  researchers 
(Sugimoto  et  al.,  2017).  This  was  to  see  if  various  approaches  led  to  converging 
findings  regarding  the  positive  effects  of  mobility  on  the  scientific  performance  of 
researchers. 

Additional  analyses  not  linked  to  specific  policy  questions  were  also  performed.  For 
example,  results  from  the  MORE3  survey4  –  which  surveyed  thousands  of  individual 
researchers working within and outside the EU-27 to investigate mobility patterns within 
and  beyond  Europe  –  were  used  to  contextualise  this  study’s  findings  as  well  as  to 
determine  if  survey-  and  publication-based  approaches  provide  converging  findings. 
Evidence of convergence would be useful in future planning of the MORE survey given its 
relatively  high  costs  compared  to  mobility  data  extracted  from  information  on 
publications. For example, the latter source could provide more regular updates on some 
of the survey questions characterising the achieved level of mobility, while the more in-
depth investigation of the factors underlying the observed mobility patterns could still be 
handled by the survey every couple of years, as is currently the case. 

4 https://www.more3.eu/surveys  

9 

 
 
 
                                                 
2  Methods 

This  section  of  the  brief  presents  the  methodological  approach  devised  to  prepare  the 
mobility  data  for  this  study.  This  approach  has  been  segmented  into  various  critical 
steps,  each  of  which  is  the  subject  of  the  below  methods  boxes.  The  methods 
descriptions  have  been  synthesised  and  simplified  to  make  them  more  accessible  to  a 
general audience of decision-makers. 

TRACKING RESEARCHERS 

To investigate mobility patterns, this study relies on disambiguated author unique 
identifiers  (AUIDs)  provided  in  the  Scopus  database;  via  the  AUIDs,  publications 
are  automatically  assigned  to  their  corresponding  researchers  using  various 
information such as author names, author affiliations, frequent keywords used by the 
authors,  frequent  co-authors,  etc.  Although  these  assignations  are  not  perfect,  they 
have  been  shown  to  provide  reliable  estimates  for  bibliometric  indicators  when 
results  are  computed  for  large  enough  samples  (i.e.  above  500,  or  ideally  1 000, 
researchers) (Campbell & Struck, 2019). 

The  most  important  limitations  related  to  the  use  of  the  AUIDs  are  linked  to 
publications wrongfully assigned to a researcher profile (i.e. an AUID or portfolio) and 
those  that  were  missed.  These  may  lead  to  either  a  false  mobility  event,  when 
publications  that  should  not  have  been  assigned  to  a  researcher  point  towards  a 
mobility event to another country, or may instead result in a missed mobility event, if 
a  publication  that  should  have  been  assigned  to  a  given  researcher  is  missed  and  it 
links the researcher to a new country. This latest limitation is particularly important as 
split  portfolios  –  when  papers  from  a  researcher  are  assigned  to  more  than  one 
AUID – are more likely to occur after a mobility event. This is because the algorithm 
creating the AUIDs relies on institutional information to build the portfolios. Therefore, 
a researcher experiencing a mobility event is more likely to see his or her output split 
between  two  or  more  AUIDs.  This  would  result  in  the  method  detecting  two 
researchers,  each  staying  in  their  original  country,  instead  of  a  single  researcher 
experiencing a mobility event out of his or her original country. 

The  effects  of  such  miscounts  are  quite  difficult  to  characterise  as  the  splitting  may 
impact  both  the  numerators  (the  number  of  mobile  researchers)  and  the 
denominators  (the  number  of  researchers  from  a  country).  However,  the  AUIDs’ 
reliability  is  generally  high,  and  this  effect  should  be  more  prominent  only  for 
researchers  with  highly  frequent  names.  Therefore,  this  method  is  less  reliable 
for  Asian  countries  where  the  distribution  of  family  names  is  proportionately  less 
varied and population counts are high (e.g. China and South Korea).  For this policy 
brief focusing on Europe, findings should be quite robust, although mobility events 
from  Asian  researchers  to  Europe  might  be  more  difficult  to  properly  assess. 
Nevertheless,  with  most  analyses  focusing  on  mobility  between  EU-27  nations, 
this limitation should have negligible impact on the reported findings. 

10 

 
 
 
FILTERING AUIDS 

Although  the  AUIDs  are  generally  quite  reliable,  a  small  share  of  them  are 
obviously wrong. To remove AUIDs with obvious problems, two filters were applied 
to the data. First, all AUIDs assigned to 10 or more countries in the database were 
removed. Second, AUIDs were removed if they linked to 10 or more of the 22 fields 
under  the  Science-Metrix  classification  of  science.  These  problematic  AUIDs  are 
usually  generated  because  the  data  are  too  ambiguous,  creating,  for  example,  large 
clusters  of  publications  linked  to  individuals  sharing  the  same  family  name  and  first 
initial. The effect of these filters is quite modest as they only remove a combined 
total  of  approximately  16 000  AUIDS  (or  less  than  0.07 %  of  the  more  than  24 
million  AUIDs  in  the  database).  However,  these  AUIDs  are  likely  to  capture  false 
mobility  events.  Accordingly,  the  above  filters  reduce  noise  in  the  data  by  removing 
multiple false mobility events from the analysis. 

ASSIGNING RESEARCHERS TO COUNTRIES 

Unfortunately,  it  is  not  possible  to  identify  the  nationality  of  researchers  to 
properly assign individuals to their native country. Because no suitable solution to this 
issue currently exists, the  country of origin of a given researcher for this brief was 
based  on  the  country  of  affiliation  appearing  on  the  researcher’s  earliest 
publications in the Scopus database (i.e. from the first publication year). In most 
cases,  this  provides  the  starting  location  of  a  researcher  during  his  or  her 
undergraduate  or  graduate  studies,  either  in  the  researcher’s  native  country  or 
abroad. The assumption is that this country generally aligns with the native country of 
a  researcher,  but  of  course  this  is  not  the  case  for  students  studying  abroad.  When 
researchers  published  with  addresses  from  more  than  one  country  in  their  first 
publication year, they were included in the starting populations for all these countries. 
This is a limitation of the approach that cannot be easily resolved unless data are 
limited to much smaller subsets for which citizenship information could be retrieved – 
for  example,  through  database  linking.  However,  such  an  endeavour  would  likely 
prove to be not only quite challenging but also not possible because of confidentiality 
issues.  Also,  such  an  approach  would  most  likely  lead  to  imprecise  results  given  the 
massive population migrations of the last century. 

11 

 
 
 
 
CHARACTERISATION OF THE FINAL POPULATION SELECTED FOR ANALYSIS 

individuals  who  pursued  a  career 

For  this  brief,  a  cohort  approach  was  the  preferred  method  for  most  analyses, 
keeping  track  of  mobility  events  per  individual  over  time;  a  single  cohort  of 
researchers  who  started  their  career  around  the  same  year  and  who  kept 
publishing  for  at  least  15  years  was  selected.  These  two  criteria  ensured 
similarity in terms of seniority across analysed individuals and acted as a filter to 
keep 
thus  excluding 
undergraduate  and  graduate  students  who  published  some  papers  during  their 
training  but left  academia  afterwards.  Individuals  starting  their  publishing  career 
between  2001  and  2003  were  kept  in  the  analysis;  this  period  was  selected  to 
ensure  enough  time  had  passed  in  a  researcher’s  careers  to  detect  mobility  up  to 
2019,  while  ensuring  that  enough  data  on  previous  publication  years  (the  version  of 
Scopus used for this study went back to 1996) were available to make sure that the 
selected researchers did not publish prior to the starting period considered (i.e. 2001–
2003). This would otherwise have indicated that they had not started their career in 
the correct time frame. Of the 2.8 million AUIDs starting to publish between 2001 and 
2003 in Scopus, 630 000 researchers, or only 22 %, were still active 15 years 
later. 

in  research, 

12 

22%78%Cohort of researchers starting between 2001 and 2003Still active after 15 yearsNot active after 15 years≈ 630,000 researchers 
 
 
 
REGIONAL ANALYSIS IN THE EU-27 

As  data  were  being  prepared,  regional  patterns  started  to  emerge  within  the  EU, 
which  generated  interest  in  the  preparation  of  data  at  this level.  While  some  studies 
look  at  the  EU  in  a  West–East  perspective,  others  instead  focus  on  the  North–
South  paradigm.  For  this  brief,  a  decision  was  made  to  separate  the  EU  into  four 
regions, relying mostly on historical and economic factors aligning with the above two 
approaches.  This  resulted  in  the  following  regions:  western  Europe,  eastern 
Europe, northern Europe and southern Europe. 

  Western  Europe:  Austria,  Belgium,  France,  Germany,  Ireland,  Luxembourg, 

the Netherlands 

  Eastern Europe: Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, 

Lithuania, Poland, Romania, Slovakia, Slovenia 

  Northern Europe: Denmark, Finland, Sweden 

  Southern Europe: Cyprus, Greece, Italy, Malta, Portugal, Spain 

13 

 
 
 
 
 
 
MEASURING MOBILITY EVENTS BETWEEN COUNTRIES 

The  approach  used  to  measure  migration  in  this  study  is  similar  to  another  study 
performed  by  Science  Europe  (Science  Europe  &  Elsevier’s  SciVal  Analytics,  2013). 
Tracking mobility requires  locating  a researcher  at different points over  his  or 
her career path.  In this study,  researchers  were  assigned to  countries according to 
the  chronology  (i.e.  publication  year)  of  their  published  output,  relying  on  their 
author  affiliations  as  reported  on  their  peer-reviewed  publications.  When 
researchers were linked to multiple countries through institutional addresses under 
a  single  year,  researchers  were  assigned  to  all  these  countries,  allowing  for 
multi-country  affiliation  to  take  place.  By  definition,  an  incoming  migration  (i.e.  a 
gained  or  new  researcher)  for  a  country  occurs  when  a  researcher  appears  for  the 
first  time  with  an  affiliation  in  said  country,  regardless  of  the  number  of 
publications  under  this  new  country,  or  of  the  researcher’s  co-affiliation  to  other 
countries.  Although  it  could  be  tempting  to  define  more  stringent  rules  to  select  a 
dominant  country,  either  based  on  a  pre-existing  affiliation  or  the  location  with  the 
dominant  share  of  output,  this  would  end  up  being  quite  arbitrary;  it  was  deemed 
more  practical  to  keep  all  signals  related  to  the  geographic  location  of 
researchers  because  assumptions  that  could  be  made  regarding  the  set  of  relevant 
countries to be considered for a given researcher would necessarily lead to erroneous 
decisions  regardless  of  the  selection  method.  For  example,  in  the  case  of  a 
researcher  affiliated  with  country  A,  starting  to  co-sign  his  or  her  publications  under 
both  country  A  and  country  B  at  some  point  in  time,  it  would  not  be  possible  to 
distinguish  between  the  various  scenarios  that  could  be  associated  with  this 
affiliation pattern (e.g.  if this researcher stayed in country A and simply added a  co-
affiliation to country B, or if he or she in fact left for a year to work in country B, while 
still maintaining ties to country A). Therefore, instead of forcing a choice, all country 
affiliations were kept. 

CAREER STAGES 

Even  though  this  study’s  approach  did  not  enable  tracking  mobility  from  place  of 
birth  to  country  of  graduate  studies,  it  was  nevertheless  assumed  to  enable 
identifying  some  migration  events,  with  or  without  return  to  home  country  as 
defined above (periods along the career path are defined in terms of years since first 
publication, which counts as year 1): 

 

 

for  training  (i.e.  within  the  5  years  from,  and  including,  first  publication 
assumed to correspond to mobility while studying); 

for career development (i.e. from training location to first academic position 
as an early career  scientist [migration from first 5 years after  first publication 
to  the  next  5  years]  or  mobility  during  the  starting  years  as  an  independent 
scholar [from year 6 to 10]); 

  and  later  in  career  (i.e.  from  career  development  phase  to  later  position  as 
an  established  researcher  [migrating  from  location  in  year  6  to  10  to  a  later 
position] or mobility later in career [from year 11 and above]. 

14 

 
 
 
 
DEFINITIONS OF INDICATORS 

Number  of  publications:  The  count  of  publications  associated  with  an  author  ID 
(AUID), acting as proxy for the total number of publications for a given researcher. 

Net gain: Difference between the number of new researchers and the number of lost 
researchers through mobility for a given country or region. 

Net gain relative to reference number: Difference between the number of gained 
and lost researchers through mobility for a given country or region, normalised by the 
reference number of researchers for that country or region (e.g. maximum number of 
researchers,  number  of  gained  researchers,  number  of  starting  researchers  [i.e. 
number  of  researchers  in  the  sample  that  started  their  publishing  career  in  a  given 
country]). 

Average  of  relative  citations  (ARC):  This  indicator  is  a  discipline-normalised 
indicator  based  on  citations  to  publications.  The  number  of  citations  per  paper  is 
normalised  against  the  average  number  in  the  same  subfield  of  science  and 
publication  year,  yielding  a  relative  citation  (RC)  score  for  each  paper.  The  ARC  is 
thus  simply  the  average  of  these  relative  citation  scores  for  papers  related  to  an 
entity. Scores above 1 indicate performance above expectations (i.e. the world level), 
while scores below 1 indicate the opposite. 

Top 1 % of highly cited publications (HCP 1 %): Highly cited publications (HCP) 
are  publications  that  have  received  RC  scores  among  the  highest  in  their  respective 
field  and  year.  Most  commonly,  the  top  1 %  most  cited  publications  (HCP1 %)  is 
considered to examine research excellence, measuring how many high-impact articles 
are  produced  by  a  given  research  entity,  relative  to  their  expected  contribution  to 
world-leading research. 

The  HCP  measure  is  normalised  to  1,  meaning  that  an  entity  with  an  HCP  over  1 
contributes  more  than  its  expected  number  of  highly  cited  papers,  an  entity  with  an 
HCP below 1 contributes fewer than its expected number of highly cited papers, and 
an entity with an HCP near 1 contributes close to its expected number of highly cited 
papers. 

Average  of  relative  CiteScores  (ARCiS):  This  is  a  journal-based  metric.  The 
CiteScore  (CS)  of  each  journal  in  a  given  year  is  measured  by  counting  the  total 
number  of  citations  that  were  received  in  that  year  by  the  papers  that  appeared  in 
that  journal  in  the  previous  five  years.  The  weighted  CiteScore  (WCS)  is  then 
obtained by dividing the total number of received citations by the number of articles 
that appeared in that journal in the previous five years.  It is thus a ratio expressing 
the  average  number  of  times  articles  and  other  citable  documents  in  a  journal  are 
cited. A journal’s score for a given year is then attributed to all its publications in the 
corresponding  year.  These  paper-level  scores  are  then  normalised  by  subfield  and 
year, prior to their aggregation using a simple average of the normalised  WCS of an 
entity’s  publications.  This  aggregation  results  in  the  ARCiS  for  the  entity.  Scores 
above 1 indicate performance above expectations (i.e. the world level), while scores 
below 1 indicate the opposite. 

3 

Results & discussion 

The next section of the policy brief is dedicated to the presentation of the main findings 
emerging  from  the  performed  analyses.  The  first  part  focuses  on  general  mobility 
statistics  and  mobility  in  earlier-career  years,  which  represents  the  training  period  for 
most individuals. The second part is dedicated to the analysis of the brain gains and brain 
drains concepts amongst researchers, identifying countries and regions gaining or losing 
the  most  in  this  regard.  The  third  section  investigates  the  effects  of  mobility  on 

15 

 
 
bibliometric  indicators  related  to  productivity  and  citation  impact.  Finally,  the  fourth 
section presents an analysis of differences in mobility across disciplines of science. 

3.1  General concepts on mobility and mobility in the training stage 

MOBILE AND NON-MOBILE RESEARCHERS 

As  mentioned  in  Section  2,  a  highly  relevant  finding  regarding  mobility  is  that  the  vast 
majority  (~80 %)  of  students  or  young  researchers  acting  as  authors  on  at  least  one 
publication  end  up  leaving  academia  after  a  few  years.  This  group  includes  individuals 
who completely left research and individuals who moved to another R&D sector in which 
publishing is not a key output (e.g. the private sector). For the remaining 20 % of cases, 
although it cannot be defined as a rare event, mobility is not the dominant career path in 
research, at least not for scholars who pursued an academic career in the selected cohort 
from  the  turn  of  the  millennium.  About  66 %  of  researchers  still  active  after  15  years 
never experienced any migration external to the first country they published in (Table 1). 
This  leaves  about  34 %  who  did  experience  a  migratory  event,  which  could  be  (1) 
moving to another country (full irreversible migration), (2) moving to a country for some 
years and coming back (e.g. sabbatical year), or (3) adding a co-affiliation with a foreign 
university  (while  retaining  an  affiliation  with  the  original  country).  In  the  last  case,  the 
event  may  not  be  a  true  mobility  event.  However,  since  it  is  not  possible  to  distinguish 
which of the affiliation countries is the country of residence (i.e. did the researcher stay 
in  the  starting  country  and  added  a  foreign  co-affiliation,  or  moved  to  another  country 
and  kept  an  affiliation  with  the  starting  country),  these  cases  are  currently  counted  as 
mobility events. The volume of such cases is limited, and they should not drastically alter 
the data presented in this brief. 

At the level of the EU-27, the proportion of researchers that are still active after 15 years 
and  that  have  never  experienced  a  mobility  event  (63 %)  is  slightly  lower  that  at  the 
world level (66 %) pointing to a slightly greater mobility of European researchers. For the 
whole of the ERA, this proportion is even smaller standing at 61 %. However, given the 
limitations  associated  with  this  study’s  methods,  the  observed  differences  between  the 
ERA,  the  EU-27  and  the  world  should  be  considered  with  care.  It  is  our  view  that  the 
scale  of  these  differences  remains  small  and  they  do  not  appear  to  highlight,  overall, 
strongly diverging migratory behaviours. 

16 

 
Table 1 

Share  of  researchers  never  experiencing  international  mobility 
during their publishing career 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark green for the lower proportions 
to dark red for the largest proportions, with proportions on par with the world level coloured white. The presented countries 
include those from the EU-27 and the European Research Area (ERA), plus INCO–EU countries. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Countries  with  the  largest  proportions  of  non-internationally  mobile  researchers  include 
Turkey (78 %), Russia (77 %), Poland (77 %), Croatia (76 %), Lithuania (75 %), Latvia 
(73 %),  Slovenia  (73 %),  China  (72 %),  Japan  (71 %),  Brazil  (71 %),  Spain  (70 %), 
Serbia  (69 %)  and  the  United  States  (69 %).  As  can  be  observed,  multiple  eastern 
European  countries  from  the  EU-27  are  present  in  the  list,  in  addition  to  historically 
closed  societies  (e.g.  China,  Japan)  and  countries  with  large  populations  (i.e.  China, 
United  States,  Brazil).  The  lower  mobility  of  these  populations  of  researchers  could  be 
the  result  of  multiple  factors,  including  incentives  in  place  to  retain  foreign  students  or 
that  their  population  of  students  and  young  researchers  consists  mostly  of  native 
researchers who do not move afterwards. In the former case, the mobility events would 
have occurred prior to a foreign student’s first publication, which was associated with its 
receiving  country  (these  events  are  undetectable  per  this  study’s  design).  In  the  latter 
case,  this  could  be  due  to  incentives  to  stay  in  the  home  country,  a  lack  of  appeal  for 
international  mobility,  a  lack  of  opportunities  in  other  countries  or  a  national  self-
reliance. That Spain is the only non-eastern EU-27 nation to appear in the above list of 
countries  with  low  mobility  levels  already  points  to  stark  differences  across  EU  regions. 
Indeed,  the  weighted  proportion  of  non-internationally  mobile  researchers  goes  from 
lows of 58 % and 59 % for western and northern Europe, respectively, to highs of 70 % 
and 73 % for southern and eastern Europe (Figure 1). 

17 

CountryCountry code% Non-mobileCountryCountry code% Non-mobileWorld66%World66%ERA61%ERA61%EU–2763%EU–2763%BelgiumBE47%LiechtensteinLI57%BulgariaBG62%NorwayNO63%Czech RepublicCZ67%SwitzerlandCH35%DenmarkDK54%IsraelIL49%GermanyDE53%MontenegroME61%EstoniaEE65%MacedoniaMK54%GreeceEL60%AlbaniaAL43%SpainES70%SerbiaRS69%FranceFR59%TurkeyTR78%CroatiaHR76%KosovoXK44%IrelandIE38%Bosnia and HerzegovinaBH49%ItalyIT69%ArmeniaAM57%CyprusCY41%AzerbaijanAZ64%LatviaLV73%BelarusBY56%LithuaniaLT75%GeorgiaGE55%LuxembourgLU30%Rep. of MoldovaMD48%HungaryHU58%UkraineUA63%MaltaMT32%Faroe IslandsFO36%NetherlandsNL55%TunisiaTN63%AustriaAT47%BrazilBR71%PolandPL77%CanadaCA46%PortugalPT64%ChinaCN72%RomaniaRO67%IndiaIN67%SloveniaSI73%JapanJP71%SlovakiaSK57%South KoreaKR61%FinlandFI64%RussiaRU77%SwedenSE54%South AfricaZA56%United KingdomUK48%United StatesUS69%IcelandIS47%AustraliaAU52% 
 
 
Figure 1 

Percentage of non-mobile researchers per EU-27 region 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Scores for regions are weighted proportions according to the 
number of researchers per country. Therefore, countries with more individuals have higher weights in the computation than 
those with fewer individuals. 
Source:  Prepared by Science-Metrix using Scopus data (Elsevier) 

Among the most mobile country cohorts are those of Luxembourg (30 %), Malta (32 %), 
Switzerland  (35 %),  the  Faroe  Islands  (36 %),  Ireland  (38 %)  and  Cyprus  (41).  The 
presence  of  multiple  countries  with  smaller  academic  systems  is  not  surprising  as 
individuals might be forced to leave these countries following their training, the retention 
capacities  of  these  countries  being  more  limited  compared  to  countries  with  larger 
academic  structures.  In  the  EU-27,  in  addition  to  Luxembourg,  Malta,  Ireland  and 
Cyprus,  the  nations  with  the  most  mobile  cohorts  include  Austria  (47 %),  Belgium 
(47 %),  Germany  (53 %),  Sweden  (54 %),  Denmark  (54 %)  and  the  Netherlands 
(55 %).  Except  for  smaller  southern  European  nations,  all  these  countries  are  part  of 
western  and  northern  Europe,  a  reflection  of  the  regional  patterns  reported  earlier. 
Globally,  other  countries  with  highly  mobile  populations  include  Canada  (46 %),  the 
United Kingdom (48 %), Israel (49 %) and Australia (52 %), followed by multiple EU-27 
members as discussed above (i.e. Germany, Denmark, Sweden and the Netherlands). 

Within  the  European  context,  Italian  and  Spanish  researchers  had  much  lower  mobility 
scores  (respectively  31 %  and  30 %  of  mobile  researchers)  than  the  weighted  average 
score measured across EU-27 countries (41 %). The divide across European nations was 
again  quite  visible  here.  With  the  exception  of  Spain  and  Italy,  the  lowest  international 
mobility  rates  within  the  EU-27  were  observed  in  the  eastern  European  nations  (Poland 
[23 %],  Croatia  [24 %],  Lithuania  [25 %],  Slovenia  [27 %],  Latvia  [27 %],  Czech 
Republic  [33 %],  Romania  [33 %]  and  Estonia  [35 %]),  while  western  and  northern 
European nations generally had the highest rates. The highest ranking eastern European 
nations were Slovakia (43 %, 12th) and Hungary (42 %, 13th), performing on par with 
the weighted average of EU-27 nations. 

Mobility  across  regions  and  sectors  during  training  may  influence  the  course  of  an 
individual’s  career  in  research.  Therefore,  work  was  also  performed  to  compare  the 
mobility patterns in the early publishing years of individuals of this study’s selected set of 
researchers  with  the  mobility  patterns  of  researchers  who  reached  the  career 
development  stage  but  did  not  publish  over  more  than  these  5  to  10  years.  Results 
clearly  demonstrate  that  scholars  who  end  up  pursuing  a  career  in  the  later  stage,  as 
defined above, are twice as likely to have experienced a migratory event in their training, 
compared  to  those  who  ended  up  leaving  research  in  the  career  development  period 
(12.6 % vs 6.6 %).  This finding illustrates a key difference: those who do not pursue  a 

18 

0%20%40%60%80%100%Eastern EuropeSouthern EuropeNorthern EuropeWestern EuropePercentage of non-mobile researchers 
 
 
research career are proportionately less mobile across borders during their training years 
(they  are  less  attractive/attracted  to  the  academic  system).  Further  work  could  be 
performed  to  investigate  whether  those  who  do  not  pursue  an  academic  career  are 
proportionately more mobile across sectors (students working in partnership with a firm 
are more likely to quit academia to pursue a research career in the private sector). 

INTERNATIONAL MOBILITY OF EARLY CAREER INDIVIDUALS (UNDERGRADUATE AND 
GRADUATE STUDENTS) 

POLICY IMPLICATIONS 

Based  on  findings  presented  in  this  section,  differences  in  mobility  per  region 
and  countries  may  highlight  a  need  for  mobility  policies  tailored  to  some  of  the 
regions  with  the  least  mobile  populations  of  researchers.  This  would  be  to 
foster a more even spread of migratory events within Europe (accounting for the size 
of countries) as it is demonstrated that mobility brings positive spillover effects to 
all nations involved. 

Additionally, since individuals who are mobile during their early training years 
are  much  more  likely  to  stay  in  research  compared  to  those  who  do  not, 
policies fostering mobility of students should be considered as a potential tool to 
mobilise  young  researchers  and  keep  them  involved  in  the  research  community 
instead, especially in countries and scientific areas where absence of available 
talents or retention problems are identified. 

Among the mobile populations, many of the mobility events detected are most certainly 
attributable  to  trainees  studying  abroad  and  coming  back  to  their  original  country. 
Focusing the analysis on mobile researchers who moved in the training stage (i.e. in the 
five  years  following  their  first  publication),  we  identified  that  7 %  of  the  34 %  who 
experienced  at  least  one  migratory  event  in  their  whole  career  (i.e.  2.3 %  of  all 
researchers)  followed  this  pattern,  only  moving  at  the  beginning  (potentially  returning 
home),  and  then  never  experiencing  any  additional  mobility  event  later  in  their  career. 
Thus, this pattern appears to be more of an exception than the main behaviour. 

Countries  netting  the  largest  positive  migratory  balance  from  these  early  migrants 
switching country while studying and then sticking with the same country for the rest of 
their  career  include  the  United  States,  China,  Germany,  Canada,  the  United  Kingdom, 
France  and  South  Korea  (Table  2).  When  normalising  against  the  number  of  leaving 
researchers to account for population size, Turkey comes first (184 %) for countries with 
a  net  balance  higher  than  50  individuals,  followed  by  South  Korea  (181 %),  Greece 
(138 %), Portugal (129 %), Austria (117 %), Norway (114 %) and China (102 %). Note 
that  these  countries  do  not  necessarily  present  with  a  high  count  of  such  migrations; 
however,  they  do  gain  in  terms  of  the  net  balance  between  incoming  and  outgoing 
researchers.  It  should  not  come  as  a  surprise  to  observe  that  a  few  of  these  countries 
come at the top amongst nations with the lowest proportions of mobile researchers (e.g. 
China, South Korea, Turkey), a potential hint that these cases might be those of students 
studying  abroad  coming  back  to  their  native  country,  after  which  they  tend  to  follow  a 
mobility  pattern  similar  to  that  of  most  researchers  native  to  these  countries  –  that  is, 
not being mobile internationally. 

19 

 
 
Table 2 

Mobility counts of researchers experiencing mobility events only in 
the first five years of their publishing career 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark red for the largest negative net 

gains to dark green for the largest positive net gains, with neutral balances appearing in white. The sum of balances is not 
neutral because although a researcher can only be counted once under the ‘Leaving’ category, this loss can be counted multiple 
times if the researcher moved to multiple countries over the course of the first five years. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

At  the  other  end  of  the  spectrum,  the  United  Kingdom,  which  accounts  for  the  second 
highest numbers of migrants (in both directions), presents one of the smallest  balances, 
at +22 %. This is not necessarily surprising as it hosts a highly reputed education system 
attracting  a  huge  volume  of  international  students  from  around  the  globe.  Accordingly, 
many foreign students (at undergraduate and graduate levels) in this country might have 
published their first Scopus-indexed paper while in the United Kingdom, prior to moving 

20 

CountryLeavingArrivingNet balanceNet balance/LeavingBelgium2012767537%Bulgaria32532166%Czechia5611256100%Denmark1271876047%Germany9581,40144346%Estonia62115250%Greece77183106138%Spain30054924983%France8451,17733239%Croatia28431554%Ireland891677888%Italy42560518042%Cyprus21412600%Latvia165500%Lithuania2025525%Luxembourg51712240%Hungary45803578%Malta84-4-50%Netherlands29250521373%Austria101219118117%Poland931384548%Portugal6514984129%Romania51691835%Slovenia84133413%Slovakia373700%Finland5511156102%Sweden18831412667%United Kingdom1,6331,99436122%Iceland1017770%Liechtenstein011-Norway7415884114%Switzerland28246918766%Israel184149-35-19%Montenegro53-2-40%North Macedonia72215214%Albania022-Serbia26421662%Turkey87247160184%Kosovo033-Bosnia and Herzegovina41814350%Armenia87-1-13%Azerbaijan (non-ERA)385167%Belarus (non-ERA)2427313%Georgia1015550%Moldova3300%Ukraine737523%Faroe Islands000-Tunisia175235206%Brazil18434516188%Canada8111,23342252%China5391,089550102%India346333-13-4%Japan64779414723%South Korea177497320181%Russian Federation23938214360%South Africa941243032%United States of America3,8395,8462,00752%Australia40870029272% 
 
 
back  to  their  home  country  or  anywhere  else  where  they  could  find  a  position  in 
research. In other words, these cases would only be counted as leaving when they later 
start publishing in another country, but they cannot be counted in the incoming balance 
at  the  beginning  as  they  are  indistinguishable  from  students  native  to  the  United 
Kingdom. Therefore, it would be risky to draw strong conclusions from these data given 
the  many  valid  hypotheses  that  could  support  the  findings  observed  here.  One  method 
that can start to address this is to analyse the  surnames  of two subgroups:  (1) ‘native’ 
individuals who never left a given country (e.g. the United Kingdom) in the first five-year 
period, which could include ‘true natives’ as well as foreign students who first published 
in  their  host  country  (‘false  natives’)  and  did  not  leave  within  5  years,  and  (2)  ‘native’ 
individuals that left the given country in the first five-year period, which could encompass 
‘true  natives’  as  well  as  foreign  students  who  first  published  in  their  host  country  prior 
returning  home.  Here,  a  reasonable  assumption  would  be  that  ‘false  natives’  are  much 
more likely to occur in the second group if the host country is a popular hub for foreign 
students. 

Results  from  a  small-scale  analysis  for  the  United  Kingdom  demonstrate  that  the 
proportion  of  Asian  surnames  among  ‘native’  students  that  left  in  the  first  five-year 
period  is  much  larger  than  the  proportion  within  the  population  of  individuals  native  to 
the country that never left. This result is a clear indication that the current method is not 
able to adequately account for all foreigners coming to study in a new country, and this 
issue will impact more heavily on the most sought-after training destinations. Therefore, 
for ‘hot’ hubs in scholarly training, this method might only be measuring some foreigners 
as they leave, negatively impacting the balance for these highly prized destinations. 

Other countries that are popular hubs for international students might also be affected by 
this,  and  they  also  generally  present  smaller  balances.  They  include  the  United  States 
(52 %), Germany (46 %), France (39 %), Japan (23 %) and Canada (52 %). Broadening 
the  surname  analysis,  the  country  appearing  to  be  most  affected  by  this  issue  is  the 
United States. Its population of ‘native’ students leaving in the first period is dominated 
by  individuals  with  Asian  names  (Chinese  and  Korean),  while  its  ‘native’  population  of 
individuals never leaving the country, although still including a high number of individuals 
with  Asian  names  given  the  diversity  of  the  US  population,  is  more  diversified  and 
includes a high share of individuals with English names (Smith, Johnson, Miller, Brown). 
Individuals with these latter names are mostly absent from the list of US students leaving 
for another  country during training.  The presence  of  Germany and France  on the above 
list  is  also  noteworthy,  these  being  the  two  largest  EU-27  members  in  terms  of 
researcher  population.  Other  EU-27  countries  presenting  with  smaller  balances  include 
Italy and Belgium. 

In terms of the migratory paths followed by these young researchers, the most frequent 
exchanges  observed  in  the  selected  populations  often  involved  the  countries  frequently 
listed above: the United States, the United Kingdom, Canada, China and Japan. For pairs 
involving  EU-27  members,  the  France–Germany  pair  is  the  most  frequent  one,  and  this 
for  both  countries,  France  attracting  slightly  more  individuals  associated  with  Germany 
than  the  reverse  (i.e.  +632  for  France,  +607  for  Germany).  Italy–France,  Austria–
Germany, Germany–Italy, Netherlands–Germany, France–Spain and Belgium–France are 
other  frequent  pairs  experiencing  hundreds  of  mobility  events  between  their  composing 
members.  Still,  overall,  the  United  Kingdom  most  often  ranks  as  the  first  dominant 
pairing country for multiple EU members, an indication of the importance of this former 
EU Member State in the mobility behaviours of the European Union. 

POLICY IMPLICATIONS 

The  UK  has  been  an  important  actor  in  the  mobility  network  of  Europe.  In  the 
aftermath of Brexit, new barriers for mobility between the UK and EU members 
could substantially alter pre-existing dynamics. Mobility in research within the EU 
will have to be rethought to address the new challenges brought by this new political 
environment.  The  importance  of  the  UK  to  the  European  network  should  thus  be 
accounted for in designing/implementing future mobility policies for the EU within and 
beyond its borders. 

21 

 
Globally,  the  most  interrelated  populations  appear  to  be  those  of  the  US–Canada  and 
US–UK  pairs,  with  the  largest  numbers  of  exchanged  individuals5.  Balance  balances 
between these countries are relatively neutral, but the United States presents a slight net 
gain in both scenarios (422 gains vs 403 losses with Canada for a positive balance of +18 
for the United States; 455 gains vs 402 losses with the United Kingdom, positive balance 
of  +43  for  the  United  States).  However,  the  US  balance  with  China  is  negative,  losing 
354 young researchers to China, while only attracting 290 (net loss of 64). Again, as with 
what  was  observed  with  the  surname  analysis  among  these  migrants,  this  negative 
balance  is  most  probably  the  result  of  a  larger  pool  of  Chinese  students  immigrating  to 
the  United  States  prior  to  starting  publishing in  journals  covered  in  Scopus  (e.g.  during 
their undergraduate studies, masters or PhDs) compared to the pool of Chinese students 
emigrating  to  the  United  States  after  a  first  Scopus-indexed  paper  produced  while  in 
China  (e.g.  again  for  their  undergraduate  studies,  masters  or  PhDs).  Students  in  the 
former  group  would  thus  publish  their  first  Scopus-indexed  paper  while  in  the  United 
States  and  would  then  move  back  to  China  (e.g.  to  complete  their  graduate  studies  or 
take a position in the Chinese academic system), and students in the latter group would 
migrate  to  the  United  States  for  a  PhD  (or  postdoctoral  fellowship)  after  their  first 
publication in China (e.g. as undergraduate students). Under this scenario, a majority of 
incoming  and  outgoing  students  would  be  of  Chinese  origin.  To  assess  the  likelihood  of 
such a hypothesis, we again looked at the surnames in both groups of individuals. They 
were  indeed  both  dominated  by  Chinese  names.  In  turn,  the  United  States  may  not  be 
losing more of its native students to China than its Chinese gains, but instead would be a 
very attractive place for the training of Chinese students at different levels of education 
(e.g. undergraduate, masters and PhDs). Additionally, one must note that these numbers 
do  not  point  toward  a  massive  swing  back  of  the  pendulum  from  the  US  universities  to 
China’s  research  infrastructures.  Overall,  only  a  handful  of  researchers  move  between 
countries  in  these  early  years,  and  migratory  balances  between  countries  are  usually 
quite neutral. 

Within  the  EU,  preliminary  analyses  tend  to  demonstrate  that  the  findings  observed  for 
the United States are not replicable – that is, there does not appear to be a critical mass 
of Chinese students being identified as native to EU  Member States, which is reassuring 
regarding the robustness of the data prepared for the EU and its Member States. 

MULTIPLE COUNTRY CO-AFFILIATIONS OF EARLY CAREER INDIVIDUALS 

Among  the  630 000  researchers  still  active  after  the  15  years  selected  in  the  analysis, 
about  30 000  (5 %)  present  with  an  affiliation  to  more  than  one  country  in  their  first 
publishing year, indicating multiple affiliations right at the start of their publishing career, 
most  probably  during  their  graduate  studies  or  perhaps  their  postdoctoral  studies,  in 
which  case  it  would  be  sensible  to  expect  that  these  multi-country  co-affiliations  could 
come  from  students  in  a  cotutelle  agreement  (i.e.  dual-degree  PhD)  or  from  students 
who maintained collaboration links with previous supervisors. To confirm this hypothesis, 
data  on  educational  background  would  be  needed,  but  these  are  unfortunately  not 
available.  Most  of  the  individuals  with  multi-country  co-affiliations  have  at  least  one 
affiliation in the United States (close to 50 %), which should not come as a surprise given 
that the United States accounts for by far the largest number of researchers in the pool 
analysed  (Table  3).  Still,  with  less  than  25 %  of  all  starting  researchers,  the  country  is 
host  to  almost  twice  as  many  individuals  with  multi-country  co-affiliations  as  expected, 
again  highlighting  the  importance  and  attractiveness  of  the  US  education  system 
globally. 

5 These are based on counts of mobile researchers and are not normalised to illustrate preferential 
partners according to population sizes. However, given the number of mobility events detected, 
normalising  could  lead  to  some  skewed  findings  as  preferred  partners  could  potentially  more 
than often be those for country pairs with only a handful of mobile individuals. 

22 

 
                                                 
Table 3 

Number  and  percentage  of  researchers  having  more  than  a  single 
country affiliation in their first publishing year 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark red for the smallest percentage 

to dark green for the largest percentage. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

However,  percentage  wise,  Switzerland  ranks  highest  in  terms  of  multi-country  co-
affiliations at the start of a career, with 17 % of AUIDs associated with the country also 
being linked to another country in the same year. Surprisingly, only about  8 % of these 
co-affiliations (i.e. 80  out of 1 000) are linked to the  European  Organization for Nuclear 
Research (CERN), a massive international organisation, and Switzerland would still come 
out  on  top  even  without  these  CERN  co-affiliations,  an  indication  that  other  parameters 
are  at  play  here.  Other  notable  countries  include  the  United  Kingdom,  which  ranks 
second  with  a  multi-country  co-affiliation  rate  of  12 %,  followed  by  Sweden  (11 %), 
Canada  (11 %),  Germany  (11 %)  and  the  Netherlands  (10 %).  At  the  other  end  of  the 
spectrum are China (3 %), Turkey (3 %), India (4 %), Poland (4 %), Brazil (4 %), Spain 
(5 %),  Japan  (5 %),  South  Korea  (5 %),  Italy  (5 %)  and  Russia  (6 %),  all  countries 
ranking  low  in  terms  of  international  mobility  as  previously  presented.  This  is  quite 
interesting as multi-country co-affiliations (possibly through cotutelle) thus appear to be 
a good predictor  of future mobility, which should not come as a  surprise as it would be 
expected  that  individuals  who  establish  ties  with  other  countries  early  in  their  career 
through training would present with higher chances of moving to these countries later on. 
Digging further into the data, a regional divide in Europe is observable between western, 
northern,  southern and eastern  Europe (Figure 2),  with northern and  western European 
countries  presenting  higher  rates  of  multi-country  co-affiliations  (i.e.  10 %  weighted 
average),  followed  by  eastern  countries  (6 %  weighted  average),  while  most  southern 
European nations present lower rates (5 % weighted average). 

23 

Starting researchersAffiliated with other country%Starting researchersAffiliated with other country%1World626,64722,607-1World626,64722,607-2ERA261,21621,8488%2ERA261,21621,8488%3EU–27191,80314,9528%3EU–27191,80314,9528%4BelgiumBE5,57164812%34LiechtensteinLI7114%5BulgariaBG1,3401229%35NorwayNO3,8623559%6Czech RepublicCZ4,1222606%36SwitzerlandCH5,8641,00717%7DenmarkDK3,86840310%37IsraelIL4,9134429%8GermanyDE29,7583,15711%38MontenegroME56916%9EstoniaEE5916210%39MacedoniaMK1511510%10GreeceEL4,5412866%40AlbaniaAL35514%11SpainES24,2051,0945%41SerbiaRS1,740694%12FranceFR32,2852,8139%42TurkeyTR10,9633653%13CroatiaHR2,009844%43KosovoXK18211%14IrelandIE2,01027314%44Bosnia and HerzegovinaBH1581811%15ItalyIT29,5901,5825%45ArmeniaAM2512912%16CyprusCY1352418%46AzerbaijanAZ174169%17LatviaLV297217%47BelarusBY644609%18LithuaniaLT984465%48GeorgiaGE2603413%19LuxembourgLU571221%49Rep. of MoldovaMD1041716%20HungaryHU2,8412519%50UkraineUA3,6622617%21MaltaMT901011%51Faroe IslandsFO11327%22NetherlandsNL11,0881,09210%52TunisiaTN1,5841238%23AustriaAT3,72043912%53BrazilBR17,6637834%24PolandPL11,2454254%54CanadaCA18,6562,07111%25PortugalPT4,3833157%55ChinaCN54,3601,6093%26RomaniaRO2,3941617%56IndiaIN19,6027054%27SloveniaSI1,255615%57JapanJP38,5221,7925%28SlovakiaSK1,5581278%58South KoreaKR11,7705985%29FinlandFI4,1843228%59RussiaRU20,0321,1326%30SwedenSE7,68286211%60South AfricaZA2,8402629%31United KingdomUK34,6414,02112%61United StatesUS146,45810,1007%33IcelandIS3154414%62AustraliaAU13,4201,31410%Number of researchersCountryNumber of researchersOrd.Country codeCountry codeCountryOrd. 
 
 
Figure 2 

Percentage  of  researchers  affiliated  to  more  than  one  country  in 
their first publication year, by EU-27 regions 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Regional scores are weighted proportions, in accordance with the 

number of researchers per country. 
Source:  Prepared by Science-Metrix using Scopus data (Elsevier) 

POLICY IMPLICATIONS 

A  correlation  between  country  co-affiliations  in  the  training  period  and 
subsequent  mobility  appears  to  be  present  when  looking  at  results  across  highly 
mobile  and  highly  non-mobile  country  populations.  To  foster  mobility,  it  thus 
appears  that  cotutelle  programmes  could  be  targeted  as  a  mechanism  to  help 
increase  mobility  for  countries  whose  researcher  populations  are  less  mobile,  acting 
as  a  mechanism  to  make  these  researcher  populations  more  mobile  or  to 
attract  individuals from  other  countries,  depending  on  the  direction  (typically  bi-
directional) of the established cotutelles. 

COMPARISON OF BIBLIOMETRIC DATA ON MOBILITY WITH SURVEY DATA 

Supporting European Commission efforts to monitor and gain insights on  the mobility of 
individuals  involved  in  research  during  their  career  trajectory,  the  MORE3  surveys 
provide the Commission with information regarding the impact of the  mobility policies in 
place.  To  assess  whether  bibliometric  data  can  replicate  some  of  the  findings  of  these 
large  and  complex  surveys,  we  compared  some  findings  of  the  MORE3  survey  and  the 
mobility data prepared for this brief. After preliminary analyses, quite striking similarities 
emerged  from  both  sets  regarding  the  mobility  of  EU-27  researchers.  After  plotting  the 
share  of  internationally  mobile  researchers  measured  using  bibliometric  data  against 
MORE3 survey data on shares of international mobility, a clear  correlation between both 
approaches  emerged. Using two different sets  of mobility data from the MORE3  survey, 
the  Pearson  correlation  coefficients  between  the  bibliometric  and  MORE3  findings  are 
roughly  equal  to  0.81  (R2    0.65  indicating  that  close  to  65 %  of  the  variance  in  the 
survey data can be explained by the bibliometric data,  Figure 3). The patterns observed 
with the bibliometric data – for instance, those related to eastern nations placing mostly 
at  the  lowest  positions  amongst  all  European  countries  –  are  also  observed  with  the 
survey  data.  Even  more  interesting  is  that  Italy’s  and  Spain’s  particularly  low  positions 
detected with bibliometric methods are also reflected in the MORE3 survey results, with 
both  countries  ranking  at  the  bottom  of  the  list  with  eastern  European  nations  such  as 

24 

0%5%10%15%20%Eastern EuropeNorthern EuropeSouthern EuropeWestern EuropePercentage of multi-country co-affiliated researchers 
 
 
 
Slovenia, Bulgaria, Estonia, Poland and Latvia. These results confirm that the bibliometric 
approach can be used to reliably uncover verifiable mobility patterns. 

Figure 3 

Correlation between MORE3 survey data and international mobility 
measured in Scopus 

Source:  Prepared by Science-Metrix using Scopus data (Elsevier) and MORE3 survey data GML4 and GMC1 
(https://www.more3.eu/online-indicator-tool?view=deliver) 

Note: 

Cohort of 2001–2003 still publishing 15 years later. 

Additional  analysis  of  MORE3  survey  data  regarding  barriers  to  mobility  are  also  quite 
interesting  as  they  reveal  important  differences  across  EU-27  regions  regarding 
perceived  barriers  (i.e.  personal,  professional,  practical  and  administrative)  to  mobility. 
Table 4 and Table 5 present the results of the regional aggregation of survey responses 
from,  respectively,  post-PhD  mobile  and  non-mobile  cohorts  at  the  national  levels. 
Overall  for  the  EU-27,  each  of  the  above  types  of  barriers  to  mobility  are  perceived  to 
similar  extents,  with  the  professional  and  practical  factors  presenting  slightly  higher 
percentages,  followed  by  personal  and  administrative  barriers;  this  finding  holds  true 
whether  one  looks  at  mobile  or  non-mobile  cohorts.  In  the  mobile  cohorts,  eastern  and 
southern  Europe  stood  out  because  their  corresponding  countries  ranked  higher,  on 
average,  in  reporting  professional  and  administrative  factors  as  barriers  to  mobility 
compared to personal and practical barriers. For personal barriers they scored similarly to 
other  regions  and  for  practical  barriers,  they  ranked  lower.  It  was  previously  reported, 
based on bibliometric data, that cohorts from these two regions tended to be less mobile 
compared  to  their  colleagues  from  western  and  northern  Europe.  This  finding,  when 
combined  with  the 
identified 
administrative  and  professional  factors  as  barriers  more  frequently  than  did 
individuals from  western  and  northern  Europe,  is  highly  relevant  to  the  design 
and implementation of mobility policies. This is further reinforced by the fact that for 
practical  and  personal  factors,  countries  from  southern  and  eastern  Europe  are  ranked 
similarly to or at lower positions than individuals from western and northern Europe. This 
is a clear indication that the lower mobility rates observed in these two regions might not 
come  from  personal  factors  intrinsic  to  individuals  in  the  corresponding  countries,  but 
instead from structural barriers. 

individuals  from  these  regions 

finding  that 

25 

LuxembourgSwitzerlandAustriaUnited KingdomBelgiumIrelandGermanyFinlandIcelandSwedenNetherlandsCyprusDenmarkLithuaniaEstoniaFranceHungaryNorwayRomaniaSpainPortugalPolandGreeceSlovakiaCzech RepublicCroatiaItalySloveniaBulgariaR² = 0.660%10%20%30%40%50%60%70%80%90%100%0%10%20%30%40%50%60%70%80%90%100%Share of internationally mobile researchersShare of international >3 months mobile R2-3-4 researchers that have changed employer in at least one of their moves, in % and by panel countryGML4: Post-PhD employer mobilityLuxembourgSwitzerlandAustriaUnited KingdomBelgiumIrelandGermanyFinlandIcelandSwedenNetherlandsCyprusDenmarkLithuaniaEstoniaFranceHungaryNorwayRomaniaSpainPortugalPolandGreeceSlovakiaCzech RepublicCroatiaItalySloveniaBulgariaR² = 0.640%10%20%30%40%50%60%70%80%90%100%0%10%20%30%40%50%60%70%80%90%100%Share of internationally mobile researchersShare of researchers currently employed in another country than their country (countries) of citizenship, in % and by panel countryGMC1: Current international mobility based on citizenship 
 
 
Table 4 

Factors  acting  as  barriers  to  mobility  as  reported  by  post-PhD 
mobile researchers in the MORE3 survey 

Note: 

Colour coding ranges from dark red for the smallest average ranks to dark green for the largest average ranks. 

Scores are non-weighted averages of country scores and rankings. 
Source:  Prepared by Science-Metrix using the MORE3 survey from indicators GMB2.1, GMB2.2, GMB2.3 and GMB2.4 

When  looking  at  the  same  data,  but  for  non-mobile  cohorts,  findings  are  generally 
similar,  except  for  the  fact  that  eastern  European  nations  ranked  on  average  much 
higher than nations from other regions on the personal factor (8.0 compared to 15.5 for 
southern  Europe,  18.0  for  western  Europe  and  20.7  for  northern  Europe),  an  indication 
that amongst those who do not move in  eastern Europe, a larger share attribute this in 
part to personal barriers. Nevertheless, eastern Europe, and southern Europe to a lesser 
extent,  also  ranked  much  higher  when  listing  professional  and  administrative  barriers, 
again an indication that there might be some structural barriers in the European mobility 
network that might hinder mobility for eastern and southern European researchers more 
so than researchers from western and northern Europe. 

Table 5 

Factors acting as barriers to mobility as reported by post-PhD non-
mobile researchers in the MORE3 survey 

Note: 

Colour coding ranges from dark red for the smallest average ranks to dark green for the largest average ranks. 

Scores are non-weighted average of country scores and rankings. 
Source:  Prepared by Science-Metrix using the MORE3 survey from indicators GMB3.1, GMB3.2, GMB3.3 and GMB3.4 

Unsurprisingly, for all four factors, the percentages of individuals perceiving barriers were 
higher  in  the  non-mobile  cohorts,  generally  by  5  to  10  percentage  points.  Also,  in  the 
mobile  cohorts,  although  patterns  were  observed  for  regions,  scores  were  generally 
closer and the averages per region across all four factors  were almost identical, ranging 
from 30 % for western Europe to 32 % for eastern Europe. However, for the non-mobile 
cohorts,  notable  differences  were  measured,  with  the  highest  average  being  measured 
for  eastern  Europe  (46 %,  ranking  of  9.7),  followed  by  southern  Europe  (41 %,  14.1), 
northern  Europe  (37 %,  16.1)  and  western  Europe  (32 %,  19.4).  Therefore,  although 
post-PhD  mobile  researchers  from  eastern  and  southern  Europe  ranked  only  slightly 
higher globally across all four factors, the difference in assessment from their non-mobile 
colleagues was much more distinct compared to those of the two other regions, again a 
clear  signal  that  mobility  within  eastern  and  southern  Europe  is  hindered  by  barriers, 
among which the most important ones appeared to be structural (i.e. administrative and 
professional factors). 

26 

ScoreRkScoreRkScoreRkScoreRkEU–2739%-26%-36%-29%-Eastern Europe45%8.724%9.628%15.029%11.6Northern Europe39%13.019%13.737%9.327%12.3Southern Europe43%11.022%12.630%14.229%11.2Western Europe31%18.321%13.641%8.126%12.9RegionProfessionalAdministrativePracticalPersonalScoreRkScoreRkScoreRkScoreRkEU–2745%-31%-47%-39%-Eastern Europe56%9.037%8.549%13.341%8.0Northern Europe37%18.723%19.059%6.031%20.7Southern Europe48%13.731%14.048%13.236%15.5Western Europe34%22.022%19.542%18.032%18.0RegionProfessionalAdministrativePracticalPersonal 
 
 
 
 
POLICY IMPLICATIONS 

Eastern European nations (and to a milder degree  southern European nations) were, 
relative  to  countries  in  other  European  regions,  much  more  likely  to  report 
professional  and  administrative  factors  as  barriers  to  mobility,  and  this  from  both 
mobile and non-mobile cohorts. Further investigations of the factors underlying these 
differences  (e.g.  through  surveys)  could  guide  the  design  and  implementation  of 
specific  interventions  aimed  at  levelling  the  playing  field  for  researcher  mobility 
across all of Europe. For example, new or updated policies that best suit the needs of 
the least mobile populations of researchers (i.e. eastern and southern  Europe)  could 
help alleviate these barriers to ensure that EU-27 researchers have, in as much as is 
feasible, equal access to mobility, be it on a factual or perceived basis. 

3.2  Brain gains amongst populations of researchers: winners and losers 

Mobility  is  attached  to  two  main  concepts:  attraction  and  retention.  Attraction  entails 
recruiting  researchers  from  foreign  nations,  and  retention  entails  retaining  an  existing 
research  workforce,  including  not  only  native  researchers  but  also  those  recruited  from 
abroad.  Unsurprisingly,  nations  experiencing  the  strongest  migratory  shifts, 
in 
proportions  of  their  overall  pool  of  researchers,  are  often  small  nations  or  those  with 
limited  research  infrastructures,  as  a  low  number  of  migrations  is  enough  to  boost  or 
decimate  their  research  workforce.  For  instance,  the  following  countries  presented  the 
highest positive brain gains (data not shown): Cook Islands (+83 % of net gain relative 
to  maximal  number  of  researchers)6,  Liberia  (+67 %),  Sierra  Leone  (+63 %), 
Afghanistan (+61 %), Suriname (+57 %), Qatar (+54 %), Luxembourg (+50 %) and the 
United  Arab  Emirates  (+50 %).  All  these  countries,  including  the  EU-27’s  own 
Luxembourg,  stand  out  for  their  small  numbers  of  starting  native  researchers  and  then 
experienced  a  huge  boost  in  numbers  of  researchers  because  of  incoming  migrations. 
Although  incoming  migrations  in  these  countries  could  include  foreigners,  it  is  possible 
they  also  encompass  natives  who  went  abroad  prior  to  publishing  their  first  paper  and 
then  came  back  home.  Further  analyses,  for  example  through  surveys  or  big  data 
analytics crossing publication with migration data7, would be needed to differentiate the 
two  mechanisms.  As  already  discussed  earlier,  surname  analyses  already  point  towards 
underestimated  counts  of  gained  individuals  for  some  countries,  as  many  incoming 
students  have  moved  prior  publishing  their  first  paper  in  Scopus,  which  is  used  to  infer 
their  country  of  origin.  Multiple  countries  see  individuals  with  Asian  names  dominating 
their populations of ‘native’ students who left the country within the first 5 years (these 
are  likely  false  ‘natives’  who  likely  moved  prior  publishing  their  first  paper  in  their  host 
country prior moving back home) in higher proportions than the share of individuals with 
Asian  names  in  their  populations  of  ‘native’  students  who  never  left.  This  is  consistent 
with  a  high  proportion  of  students  only  publishing  their  first  paper  once  they  moved 
abroad for training. 

For China, which has a more homogenous population than most other countries, analyses 
demonstrate  that  of  the  about  1 200  foreign  individuals  that  came  to  China  and  only 
moved  in  the  training  period,  the  vast  majority  had  a  Chinese  surname.  This  either 
indicates  that  individuals  of  Chinese  ancestry  outside  China  have  a  higher  chance  of 
studying in China or that these individuals are in fact Chinese students who went abroad 
for  training,  first  published  in  Scopus-indexed  journals  while  abroad,  and  then  returned 
to  China  at  the  end  of  their  training.  While  the  former  hypothesis  is  not  impossible,  it 

6 Maximum  number of researchers is the unique count of distinct researchers affiliated to a given 

country at any point in time within this study’s sample of 626 647 researchers. 

7  Access  to  such  data  is  of  course  subject  to  ethical  questions  and  hard  (if  not  impossible)  to 

obtain. 

27 

 
 
                                                 
does  not  seem  as  plausible  as  the  latter  one,  or  at  least  it  would  appear  to  be  of  an 
insufficient  magnitude  to  explain  the  above  observation.  Therefore,  one  could  draw  the 
conclusion that the migratory balance measured for  China is strongly inflated and could 
in  fact  become  negative  if  it  was  possible  to  systematically  correct  for  this.  For 
comparison, similar analyses for the United Kingdom and the United States demonstrated 
different  patterns,  with  the  United  Kingdom  seeing  a  mix  of  Asian  names  and  English 
names, a possible indication that the United Kingdom’s gained migrants are in fact a mix 
of  foreigners  and  British  individuals  coming  back,  while  the  US  list  is  dominated  with 
individuals with Asian names, which probably indicates that the foreigners coming to the 
United States for training are more prominent in the data than US students coming back 
to the United States. 

As  mentioned,  less  attractive  destinations  –  either  because  of  limited  opportunities  in 
smaller  nations,  or  other  factors  (social,  environmental,  political,  religious)  –  often 
already  have  smaller  research  populations  yielding  higher  rates  of  brain  gain/drain  with 
relatively  small  numbers  of  migrating  researchers.  Among  the  countries  fairing  worst, 
only a few have a population of more than a few hundred researchers: Bhutan (-25 %), 
Samoa  (-23 %),  Equatorial  Guinea  (-22 %),  Zimbabwe  (-18 %),  Somalia  (-14 %), 
Vanuatu  (-14 %),  Dominica  (-11 %),  Venezuela  (-10 %)  and  Cuba  (-9 %).  As  can  be 
seen, this list mostly includes small nations, poorer countries and countries shackled by 
political crisis or economic embargoes. 

Turning now to the usual selection of countries from the EU-27, ERA and EU-INCO (Table 
6)  and  looking  at  all  migrations,  regardless  of  career  stages  when  they  occurred,  the 
biggest  attractors,  in  absolute  terms,  were  the  United  States  with  a  gain  of  ~65 000 
researchers (loss of ~59 500, for a positive net gain of ~5 500, or 3 % of total starting 
population),  followed  at  a  distance  by  the  United  Kingdom  with  26 000  gained 
researchers  (loss  of  ~24 500,  positive  net  gain  of  ~1 500,  or  4 %  of  starting  total 
population),  Germany  with  ~17 200  gained  researchers  (loss  of  ~17 000,  positive  net 
gain  of  ~200,  or  1 %  of  starting  population),  Canada  with  ~13 700  gained  researchers 
(loss of ~12 000, positive net gain of ~1 700, or 9 % of starting population), France with 
~13 100  gained  researchers  (loss  of  ~12 700,  positive  net  gain  of  ~400,  or  1 %  of 
starting population) and China with ~11 400 gained researchers (loss of ~6 500, positive 
net gain of ~4 900, or 9 % of starting population). Because of China’s low number of lost 
researchers,  its  net  balance  is  almost  identical  to  that  of  the  United  States,  the  largest 
amongst all countries. This resulted in China achieving a much higher brain gain amongst 
researchers (i.e. +~9 %), expressed in percentage of the starting population, than many 
other countries. Indeed, among countries with at least 10 000 researchers, only Australia 
(+23 %)  and  South  Korea  (+11 %)  present  higher  percentages8.  Again,  as  reported 
earlier  in  analyses  focusing  on  migrations  during  the  training  period,  it  is  possible  that 
the vast majority of these migrants going to China are native to China and only studied 
abroad before coming back. Nevertheless, these data inform us about the capacity of the 
Chinese  research  system  to  repatriate  individuals  trained  in  foreign  academic  systems, 
which in the end is also critical to a national research ecosystem. 

8  Switzerland  also  stands  out  with  a  net  gain  of  +30 %,  but  on  only  about  5 800  starting 

researchers, resulting in a net balance of +1 800 researchers. 

28 

 
                                                 
Table 6 

Gained and lost researchers per country 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Colour coding range from dark green for the smallest percentage 

to dark red for the largest percentage, with score on par with the EU-27 levels displayed in white. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Within the EU-27, in addition to Germany and France, other notable European attractors 
in  terms  of  net  numbers  of  individuals  attracted  include  mostly  non-eastern  European 
countries  like  Italy  (+6 600),  Spain  (+6 600),  the  Netherlands  (+6 100),  Sweden 
(+4 500) and Belgium (+3 900). When normalising by starting populations, Luxembourg 
(+300,  +326 %),  Cyprus  (+400,  +182 %),  Ireland  (+2 300,  +24 %),  Malta  (+60, 
+19 %),  Austria  (+3 100,  +16 %)  and  Denmark  (+3 800,  +15 %)  present  the  highest 
ratios of net gains versus starting populations. 

29 

Ord.CountryCountry codeMax. number of res.Starting number of res.Number of gained res.Number of lost res.Number of lost starting res.Number of lost gained res.Lost from Start / StartLost from Gained/ Gained(Gained-Lost) / Start2ERA379,061259,033148,597109,58637,71071,87615%48%15%3EU–27268,789190,82197,95472,19524,80047,39513%48%13%19LuxembourgLU369573121261611028%35%326.3%16CyprusCY5381344041602014015%35%182.1%14IrelandIE4,2961,9912,3051,8235161,30726%57%24.2%21MaltaMT148905841162518%43%18.9%23AustriaAT6,8293,6963,1332,5507611,78921%57%15.8%7DenmarkDK6,6293,8462,7832,1896101,57916%57%15.4%9EstoniaEE815589226141361056%46%14.4%25PortugalPT6,3534,3222,0311,4273331,0948%54%14.0%17LatviaLV3972971006818506%50%10.8%6Czech RepublicCZ5,3374,0971,2409452786677%54%7.2%27SloveniaSI1,6271,240387313542594%67%6.0%30SwedenSE12,1167,6344,4824,0441,3272,71717%61%5.7%10GreeceEL6,0304,5071,5231,30252677612%51%4.9%18LithuaniaLT1,16297818413850885%48%4.7%4BelgiumBE9,3935,5103,8833,6381,2052,43322%63%4.4%11SpainES30,70424,1106,5945,5931,7553,8387%58%4.2%29FinlandFI5,7084,1611,5471,41347593811%61%3.2%26RomaniaRO2,9732,3875865242163089%53%2.6%22NetherlandsNL17,18811,0126,1765,9081,9983,91018%63%2.4%12FranceFR45,29132,18913,10212,6974,4988,19914%63%1.3%13CroatiaHR2,3282,0073213011161856%58%1.0%8GermanyDE46,83329,62517,20817,0276,10310,92421%63%0.6%24PolandPL12,57811,2111,3671,3295387915%58%0.3%5BulgariaBG1,6651,33732832514518011%55%0.2%15ItalyIT36,04029,4146,6267,0372,6754,3629%66%-1.4%20HungaryHU3,4992,83166871533038512%58%-1.7%28SlovakiaSK1,9431,54939442118523612%60%-1.7%36SwitzerlandCH13,9645,8218,1436,4011,8094,59231%56%30%31United KingdomUK60,60734,39726,21024,6708,76715,90325%61%4%59CanadaCA32,23018,57313,65711,9564,0807,87622%58%9%55Faroe IslandsFO2011932118%11%55%52Rep. of MoldovaMD1531025143182518%49%8%65South AfricaZA4,9092,8212,0881,5484971,05118%50%19%50BelarusBY79664215421511310218%66%-10%37IsraelIL6,3794,8871,4921,77184592617%62%-6%45KosovoXK5818402031717%43%111%67AustraliaAU23,69113,31610,3757,2982,0345,26415%51%23%40MacedoniaMK3071511561512312815%82%3%48ArmeniaAM3302517982384415%56%-1%33IcelandIS5463082381654611915%50%24%39MontenegroME9455393282415%62%13%34LiechtensteinLI427352011914%54%214%66United StatesUS210,546145,89764,64959,58119,64339,93813%62%3%53UkraineUA4,1983,66253678346731613%59%-7%51GeorgiaGE411260151115338213%54%14%61IndiaIN23,66819,4944,1743,6121,7761,8369%44%3%62JapanJP45,33238,4506,8828,2953,4584,8379%70%-4%35NorwayNO6,1663,8462,3201,5013321,1699%50%21%41AlbaniaAL743539133109%26%74%49AzerbaijanAZ239174654514318%48%11%46Bosnia and HerzegovinaBH3041581467611657%45%44%63South KoreaKR15,53911,7233,8162,4997531,7466%46%11%64RussiaRU22,93219,9972,9352,7171,2281,4896%51%1%42SerbiaRS2,1111,7273843311032286%59%3%58BrazilBR20,74617,6063,1402,4248711,5535%49%4%56TunisiaTN2,0061,578428241641774%41%12%60ChinaCN65,25453,85711,3976,4922,0054,4874%39%9%43TurkeyTR12,50210,9381,5649733376363%41%5% 
 
 
A  European  regional  analysis  presented  in  Table  7  demonstrates  that  eastern  European 
countries  occupy  most  of  the  last  positions  amongst  EU-27  countries  according  to  not 
only net balances but also net balances as a percentage of maximum population9. These 
findings highlight the fact that countries from this region are amongst the least frequent 
destinations for foreign researchers. Western Europe ranks first in terms of net balance, 
with an average position of its Member States at 7.9, and second in terms of normalised 
net  balance  (11.3),  behind  southern  Europe  (10.3).  However,  southern  Europe’s  rank 
according to normalisation by maximum population appears inflated because of its many 
small-sized countries benefiting from large increases once normalised. To control for this, 
both  indicators  were  recomputed  for  all  regions,  including  only  countries  with  at  least 
1 000 researchers in their maximum populations. With these corrections applied, western 
Europe  ranked  first  for  net  balance  (6.0)  and  second  for  normalised  net  balance  (8.3), 
while southern Europe ranked second for net balance (7.0) and third for normalised net 
balance  (9.5),  and  northern  Europe  ranked  respectively  third  and  first  for  these 
indicators.  However,  in  both  cases,  regardless  of  countries  included,  eastern  Europe 
consistently  ranked  last  for  both  indicators  and  by  quite  large  margins,  a  clear  signal 
confirming  the  lack  of  attractiveness  compared  to  other  EU-27  regions.  Poland  was  the 
highest  ranking  eastern  European  nation  at  14th  place,  recruiting  ~1 400  new 
researchers. In fact, Cyprus, Luxembourg and Malta were the only non-eastern European 
countries  ranking at positions lower than 14th place, and these  countries all have small 
academic systems. 

Table 7 

Average  position  of  countries  per  EU-27  region,  in  terms  of  net 
gains  of  individuals  and  percentages  against  maximum  population 
size 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Ranking is in descending order of net balance. Colour coding thus 

ranges from dark green for lowest (best) possible position (1) to dark red for the highest (worst) possible position (27 for all 
countries and 22 for those with a maximum population of at least 1 000 researchers over the study period). 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Even  though  some  filters  were  applied  earlier  to  control  for  countries  with  smaller 
maximum  populations,  it  is  still  striking  to  observe  that  most  of  the  countries  ranking 
lower  are  those  with  small  maximum  populations.  For  instance,  the  Pearson  correlation 
coefficient  between  maximum  populations  of  researchers  and  number  of  gained 
researchers  equals  0.89.  Therefore,  the  performance  of  eastern  European  countries 
might be in part attributable to more limited structural capacities for hosting researchers, 
and not necessarily a lack of attractiveness. Still, with the multiple findings signalling that 
eastern  European  nations  often  rank  lowest  regarding  mobility  attributes,  it  is  to  be 
expected  that  this is  not,  at  least,  the  sole  explanation  for  these  results.  This  finding is 
quite  telling  as  it  exhibits  the  stronger  attractiveness  of  non-eastern  European  nations 
compared  to  their  eastern  counterparts,  which  can  have  important  consequences  in 
terms  of  competitiveness  within  the  EU  (e.g.  non-eastern  nations  attracting  more 
researchers may end up monopolising highly skilled scientists). 

In western Europe, the countries that stood out most include Luxembourg (50 %) Ireland 
(11 %)  and  Austria  (8 %).  In  eastern  Europe,  they  include  Estonia  (10 %)  and  Latvia 

9 The maximum number of researchers in a country’s population from 2001 to 2019. 

30 

Average Rank Net BalanceAverage Rank % Net BalanceAverage Rank Net BalanceAverage Rank % Net BalanceEastern Europe20.118.217.815.7Northern Europe9.712.79.78.0Southern Europe12.210.37.09.5Western Europe7.911.16.08.3RegionAll CountriesMax pop of at least 1,000 researchers 
 
 
                                                 
(8 %).  In  the  north,  Denmark  stood  at  9 %.  Cyprus,  a  small  island  in  the  south,  also 
achieved a notable score of 45 %. The best performers include many small countries that 
naturally rely more heavily on foreign researchers to build their research workforce; also 
recall  that  smaller  countries  can  more  easily  achieve  outlier-like  behaviour  on  this 
dimension. Altogether, there are very few countries with a brain drain (i.e. Italy, Hungary 
and Slovakia) and it was not pronounced for any of them (-1 % in all three cases)10. 

Table  8  further  explores  inter-regional  mobility  within  Europe.  To  achieve  this,  the 
country gains and losses across EU regions were compiled11. In terms of mobility events, 
it is not surprising to see that  western Europe and southern Europe are the two regions 
with  the  largest  numbers  of  gains  and  losses,  as  these  two  regions  include  the  most 
populous EU members states (Table 8). The net balances per region, in absolute number 
of  researchers,  do  not  drastically  diverge  from  neutrality.  However,  it  can  still  be 
observed  that  countries  from  northern  Europe,  overall,  present  the  highest  normalised 
internal  mobility  balance  (i.e.  mobility  with  other  EU-27  countries)  at  +4 %  (~1 000 
researchers), followed by western Europe at 2.6 % (~3 300 researchers), eastern Europe 
at  +1.5 %  (~500  researchers)  and  southern  Europe  (~1 000,  1.3 %).  Apart  from 
northern Europe,  which scored noticeably higher than the other  regions, the normalised 
internal mobility balances are relatively small and are quite similar across regions. Thus, 
no  major  brain  drains  that  would  favour  some  regions  over  other  ones  appear  to  be  at 
play here. Still, the same repeating pattern placing  northern and western  Europe ahead 
of southern and eastern Europe is observable here. 

Table 8 

Intra-,  inter-  and  external  EU-27  mobility  balances  per  European 
region 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Mobility events are captured for each EU-27 country individually, 
and counts of gained and lost researchers are then aggregated by European regions to compute a net balance. The balance is 
the ratio of the aggregated net balance and the sum of the maximum number of researchers detected per country within a 
region. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

When  counting  inter-regional  EU-27  mobility  instead  (i.e.  from  region  to  region, 
excluding  intra-regional  mobility),  relatively  similar  findings  emerge,  with  northern 
Europe  again  ranking  first  (+3.5 %)  followed  at  a  distance  by  western  Europe  (1.4 %), 
eastern Europe (1.1 %) and southern Europe (1 %). The largest difference between both 
analyses is observed for western Europe (from 2.6 % to 1.4 %). This indicates that there 
is  proportionately  a  high  share  of  intra-regional  mobility  events  within  this  region  (i.e. 
between  its  Member  States)  compared  to  other  regions.  A  similar  yet  less  pronounced 

10 As researchers may have multiple affiliations, there are more gained researchers than lost ones 

across countries, resulting in most countries presenting neutral or positive balances. 

11 Gains are compiled as additions to previously existing affiliations. Therefore, a gain for a specific 
country or region does not necessarily mean a loss for another one, as affiliation links are often 
maintained  with  previous  institutions.  Therefore,  it  is  normal  that  the  sum  of  additions  and 
losses across EU regions is not completely neutral as some additions are thus not counted as 
losses  for  another  country  or  region.  The  same  is  true  for  losses  as  these  may  be  counted 
multiple times for a single researcher if the researcher developed affiliations with multiple other 
countries, which will then count as a loss toward all these countries. Therefore, readers should 
be  careful  when  analysing  the  magnitude  of  the  effect;  comparisons  between  countries  and 
rankings are more reliable. 

31 

Maximum number of Gained fromLost to% BalanceGained fromLost to% BalanceGained fromLost to% BalanceEastern Europe34,3243,4882,9591.5%2,5552,1791.1%3,1662,8171.0%Northern Europe24,4534,4203,4334.0%3,5582,7043.5%5,8285,0143.3%Southern Europe79,8138,0737,0001.3%5,9625,1661.0%11,74810,3521.7%Western Europe130,19921,89618,5522.6%11,6539,7721.4%30,55129,3500.9%With other EU–27 countriesWith other EU–27 regionsWith non-EU–27 countriesRegion 
 
 
                                                 
difference is observed for northern Europe, with its score going from 4.0 % to 3.5 %, an 
indication of high mobility between Sweden, Denmark and Finland. 

Finally, regarding mobility events with countries external to the  EU-27, northern Europe 
again  ranks  first  (3.3 %),  but  this  time  followed  by  southern  Europe  (1.7 %),  eastern 
Europe  (1.0 %)  and  western  Europe  (0.9 %).  This  result  for  western  Europe  might 
appear  surprising  at  first  given  previous  findings,  but  it  is  important  to  remember  that 
western  European  countries  might  include,  in  their  starting  populations,  more  foreign 
students incorrectly tagged as ‘native students’ than eastern and southern Europe. These 
cases are not detected as gains but rather as losses if and when they returned home. 

POLICY IMPLICATIONS 

Most  regional  findings  point  towards  northern  and  western  Europe  as  the  main 
attractors in the EU-27, and this for both internal and external mobility  events. 
Therefore,  even  though  no  major  brain  drains  were  detected  within  the  EU-27,  it 
would appear relevant, from a policy perspective, to keep in mind that preferential 
destinations are more than often focused in these two regions, which should 
affect the way future policies are drafted and implemented. 

RETENTION OF STARTING POPULATIONS 

While  the  previous  analysis  informed  us  on  the  net  gains  globally  in  populations  of 
researchers,  one  additional  piece  of  relevant  information  pertains  to  a  country’s  (or 
region’s)  capacity  to  retain  its  starting  population,  which  highlights  the  capacity  of 
nations  to  retain  either  (1)  native  researchers,  or  (2)  foreign  students  trained  in  their 
system. In both cases, this is quite important as these are individuals coming out of the 
national  education  systems  of  nations.  These  data  are  presented  in  Table  6  (see  ‘Lost 
from Start / Start’ column, which shows the number of lost starting researchers over the 
starting  population  of  researchers)  and  reveal  that  the  EU-27  countries  losing  the 
smallest  share  of  their  starting  populations  are  predominantly  those  from  eastern 
European nations: Slovenia (4 %), Poland (5 %), Lithuania (5 %), Croatia (6 %), Latvia 
(6 %), Estonia (6 %), Czech Republic (7 %), Spain (7 %) and Portugal (8 %). This result 
is again not surprising given that, based on mobility numbers, eastern European nations 
rank  the  lowest  regarding  attraction  of  foreign  researchers.  Therefore,  these  countries 
likely  have  (1)  smaller  shares  of  their  ‘starting  population’  being  foreigners  who  may 
return  home  after  their  graduate/post-graduate  studies,  and  (2)  fewer  of  their  ‘true 
native’  individuals  leaving  to  foreign  nations,  as  the  MORE3  data  revealed  that  eastern 
European  researchers  were  far  more  likely  to  report  barriers  to  mobility.  This  also  puts 
into  perspective  the  fact  that  although  these  countries  do  not  attract  a  lot  of  foreign 
researchers,  their  migratory  balance  is  still  usually  mostly  neutral  or  even  slightly 
positive;  they  do  not  gain  many  new  researchers,  but  do  not  lose  many  either.  At  the 
opposite  end  of  the  spectrum,  nations  losing  the  highest  share  of  their  starting 
populations  include  Luxembourg  (28 %,  based  on  less  than  60  researchers),  Ireland 
(26 %),  Belgium  (22 %),  Germany  (21 %),  Austria  (21 %),  the  Netherlands  (18 %), 
Malta  (18 %),  Sweden  (17 %)  and  Denmark  (16 %).  Despite  these  important  losses, 
none  of  these  countries  have  a  negative  migratory  balance  as  they  also  attract  many 
researchers. 

32 

 
 
 
The  United  States  and  the  EU-27  have  similar  retention  performance,  losing  13 %  of 
their  starting  researchers12.  These  performances  are  only  exceeded  by  a  few  countries 
outside  the  EU-27,  including  Turkey  (3 %),  Tunisia  (3 %),  China  (4 %),  Brazil  (5 %), 
Russia  (6 %),  Serbia  (6 %),  South  Korea  (6 %),  Bosnia  and  Herzegovina  (7 %), 
Azerbaijan  (8 %),  Albania  (9 %),  Japan  (9 %),  Norway  (9 %)  and  India  (9 %).  As 
discussed  for  eastern  European  nations,  it  can  be  hypothesised  that  these  higher 
retention  rates  are  inflated  by  the  isolationist  nature  of  many  of  these  countries.  They 
would  have  fewer  foreign  students  appearing  in  the  starting  populations  as  false  native 
researchers,  thus  decreasing  the  proportion  of  their  starting  populations  leaving  when 
they  return  home  or  find  a  position  elsewhere.  Alternatively,  their  ‘true  native’ 
researchers may have fewer incentives (or more barriers) to migrate to other nations. 

IDENTIFYING THE MAIN ATTRACTORS IN THE RESEARCH COMMUNITY 

Faced  with  all  these  data  highlighting  thousands  of  mobility  events  in  the  EU,  it  is  of 
interest  to  examine  where  these  researchers  are  leaving  for,  and  if  there  are  notable 
variations across countries as to which countries are the main attractors for researchers? 
For EU-27 countries, the main attractor in absolute terms was the United States: it was 
the main migratory destination for 19 countries (Table 9).  The United Kingdom  was the 
next most popular destination, for four countries (Malta, Lithuania, Ireland and Greece). 
Germany  was  the  main  draw  for  two  countries  (Austria  and  Bulgaria),  while  Turkey, 
Slovakia, France, the Czech Republic and Brazil were the main attractors for researchers 
of one Member State each. In each of these cases, the country of origin shared a border 
or  a  colonial  tie  with  the  destination,  (respectively  Cyprus,  Slovenia,  Luxembourg, 
Slovakia and Portugal). It is worth pointing out that although the EU is relatively close to 
Asia,  mobility  events  to  Asian  countries  were  not  mainstream.  It  will  be  interesting  to 
repeat the same analyses in the future to see if this changes over the course of the Asian 
century13.  Currently,  when  measuring  average  rankings  of  all  destinations  for  all  EU-27 
countries, the country with the highest average rank across all 27 nations was the United 
States  (1.3),  followed  by  the  United  Kingdom  (2.7),  Germany  (3.4),  France  (6.1),  Italy 
(7.0), Switzerland (8.4), Canada (9.8), Spain (10.3), the Netherlands (10.7) and Sweden 
(11.2)14. 

12  This  is  based  on  all  international  mobility  events,  including  those  from  EU-27  nations  to  other 
EU-27  nations.  Therefore,  it  is  expected  that  the  retention  rate  of  the  EU-27  as  a  unified 
structure is in fact higher than that of the US, as EU-27 nations are often preferred destinations 
for EU-27 researchers. 

13 https://www.adb.org/publications/asia-2050-realizing-asian-century  

14 Data not presented. 

33 

 
                                                 
Table 9 

Top  mobility  destinations  of  EU-27  countries,  globally  and  within 
the EU-27 

Note: 

Cohort of 2001–2003 still publishing 15 years later. EU-27 destinations are bolded and coloured in green in the table. 

When there was a tie for first position, both destinations are presented. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Limiting to mobility within the EU-27, the main attractor shifts to Germany15. Germany is 
the favoured choice of 14 nations, including 7 eastern European nations (all but Slovakia, 
Estonia  and  Romania),  4  western  European  nations  (Austria,  Netherlands,  France  and 
Ireland),  2  of  the  3  northern  European  nations  (Finland  and  Sweden),  and  1  southern 
European nation (Greece). Here again, we see examples of how close proximity, as well 
as cultural, social and political aspects have a strong impact on mobility patterns. France 
ranks  as  the  top  destination  for  six  countries  covering  most  EU  regions  (Belgium, 
Germany, Luxembourg, Spain, Italy and Romania). The only other country acting as the 
top destination for multiple EU-27 countries is Greece, which attracts the highest number 
of EU-27 researchers from both Cyprus and Malta. 

The  above  findings  based  on  the  average  rankings  of  all  destinations  for  all  EU-27 
countries  are  reflected  when  comparing  the  average  ranking  of  each  EU-27  nation  as  a 
destination  for  researchers  from  all  other  EU-27  nations,  with  Spain,  Italy  and  the 
Netherlands  all  ranking  close  to  the  top  (Table  10).  While  Germany  clearly  ranks  first 
with an average rank of 1.6, having received ~3 000 researchers, it is followed by France 
(3.3,  ~2 600  researchers),  Italy  (3.7,  ~1 600  researchers),  Spain  (5.7,  ~1 400 
researchers),  Sweden  (5.8,  ~850  researchers),  and  the  Netherlands  (6.0,  ~1 300 
researchers). 

15 The United Kingdom was acting as the main attractor within the EU prior to Brexit. 

34 

EU–27 CountryTop Destination(World)Number of researchersTop Destination(EU–27)Number of researchersAustriaGermany356Germany356BelgiumUnited States322France321BulgariaGermany38Germany38CroatiaUnited States40Germany19CyprusTurkey38Greece12Czech RepublicUnited States113Germany66DenmarkUnited States216Sweden100EstoniaUnited States16Finland14FinlandUnited States158Germany77FranceUnited States1,530Germany607GermanyUnited States1,946France632GreeceUnited Kingdom223Germany74HungaryUnited States134Germany69IrelandUnited Kingdom438Germany61ItalyUnited States1,005France429LatviaUnited States6Germany5LithuaniaUK/US13Germany8LuxembourgFrance15France15MaltaUnited Kingdom13Greece/Italy2NetherlandsUnited States622Germany349PolandUnited States248Germany112PortugalBrazil171Spain88RomaniaUnited States70France51SlovakiaCzech Republic92Czech Republic92SloveniaSlovakia25Slovakia25SpainUnited States794France315SwedenUnited States420Germany176 
 
 
                                                 
Table 10 

Average  ranking  of  EU-27  countries  as  mobility  destinations,  for 
researchers of other EU-27 countries, and globally 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark red for destinations ranking 
lower amongst European counterparts compared to their position as a destination globally, to dark green for those ranking 
higher instead as a destination for researchers within the EU-27 compared to their position as a destination globally. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Globally, EU-27 countries mostly keep the same ranking between themselves as the most 
frequent  destinations  for  researchers  from  all  around  the  world,  with  only  a  few 
exceptions such as Ireland (11th with all countries, 15th in EU-27), Greece (15th with all 
countries, 12th in EU-27), the Netherlands (fourth with all countries, sixth in EU-27) and 
Poland  (12th  with  all  countries,  10th  in  EU-27).  It  is  worth  pointing  out  that  when 
accounting for all countries, Germany’s average rank was 5.1, closely followed by France 
at 5.3, making these two countries a top 5 destination worldwide for mobile researchers, 
only  behind  the  United  States  (1.9,  1st)  and  the  United  Kingdom  (3.6,  2nd).  Other  top 
destinations  globally  include  Canada  (6.2,  5th),  Australia  (7.5,  6th),  Italy  (8.3,  7th), 
Switzerland (8.3, 7th), Japan (8.5, 9th) and the Netherlands (8.7, 10th). 

Regional  patterns  are  clearly  apparent  in  these  data,  with  western  and  northern 
European  regions  ranking  higher  than  southern  and  eastern  nations  as  most  frequent 
destinations (Table 11). 

35 

Average rankingRkNumber of researchers receivedRanking (Average position)RkNumber of researchers received∆ RkEU vs WorldGermany1.613,0205.118,6490France3.322,6105.327,5410Italy3.731,5798.333,8630Spain5.741,3899.453,9841Sweden5.858529.862,2181Netherlands6.061,3348.743,102-2Belgium6.771,02910.471,9490Austria7.3887411.781,6350Denmark9.3956512.491,3290Poland9.71039313.7128842Portugal10.41149513.1101,205-1Greece10.51234114.6151,0243Czech Republic10.81336114.1147441Finland11.01433514.013799-1Ireland12.01527713.2111,308-4Hungary12.71622515.9164920Romania13.51720416.9173580Slovakia13.81821117.7183310Slovenia15.31910518.4202041Bulgaria15.52011318.119214-1Lithuania16.1217619.2221321Croatia16.2228118.621208-1Estonia16.5236919.6241361Luxembourg16.62412219.323172-1Cyprus17.9256319.6252080Latvia18.4262820.926530Malta19.0271521.327420For EU–countriesFor all countriesDestination 
 
 
 
 
Table 11 

Average  ranking  of  EU-27  regions  as  migratory  destinations  of 
researchers for other EU-27 regions 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Rankings are computed at the level of country pairs, then 

aggregated by regions. Colour coding within a destination region ranges from dark red for the lowest possible average rankings 
to dark green for the highest possible average rankings. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Table  12  presents  the  average  rankings  of  each  regional  destination,  disaggregated  per 
EU-27  country:  this  to  explore  if  there  are  exceptions  to  the  clear  regional  patterns 
depicted  above.  For  example,  do  the  mobility  patterns  of  some  countries  differ  from 
those  of  other  nations  in  their  region?  Overall,  only  a  handful  of  countries  present 
deviations from the regional patterns of mobility discussed above. In all cases, these are 
nations  with  small  populations  of  mobile  researchers,  resulting  in  many  ties  in  the 
rankings  of  countries  as  their  preferred  destinations.  In  turn,  for  these  countries  these 
ties  result  in  higher  average  scores  towards  most  regions.  This  is  the  case  for  Latvia, 
Cyprus,  Malta  and  Luxembourg,  which  present  averaged  rankings  across  regions  of  3.5 
to  7.0,  much  higher  than  most  countries  (average  of  11.3  across  the  other  EU-27 
nations). But even with these higher scores, the general patterns detailed above are still 
mostly  present  for  these  countries,  with  western  Europe  ranking  first  for  all  countries 
except  for  Malta,  where  it  comes  second  behind  southern  Europe,  and  Latvia  for  which 
northern  Europe  ranks  first  (5.0),  slightly  above  western  Europe  (5.3).  Most  notably, 
eastern  Europe  clearly  ranks  last  for  most  countries,  with  an  average  position  of  14.4, 
behind  southern  Europe  (11.1),  northern  Europe  (8.5)  and  western  Europe  (7.6).  The 
only countries for which eastern Europe does not rank last are the Czech Republic (14.2, 
3rd, ahead of southern Europe at 15.0), Slovakia (13.2, 3rd, ahead of southern Europe at 
13.5) and Slovenia (10.7, 2nd, ahead of southern Europe at 12.8 and northern Europe at 
11.7).  Notably,  all  these  countries  are  themselves  part  of  the  eastern  European  region, 
again  in  line  with  findings  presented  earlier  at  Table  11.  Eastern  European  nations 
presented  the  lowest  average  ranking  (i.e.  most  attracted)  across  all  four  regions  for 
mobility  towards  eastern  European  nations  (i.e.  12.9),  even  though  the  region  still 
ranked last as a destination for eastern Europe in the end. 

36 

Eastern EuropeNorthern EuropeSouthern EuropeWestern EuropeEastern Europe12.99.011.48.2Northern Europe17.14.013.78.9Southern Europe13.38.69.16.5Western Europe16.49.711.47.2Regions (Source)Region (Destination) 
 
 
Table 12 

Average  ranking  of  EU  regions  as  mobility  destinations  of  EU-27 
nations 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Colour coding within a destination region ranges from dark red 

for the lowest possible average rankings to dark green for the highest possible average rankings. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

POLICY IMPLICATIONS 

The regional preferences in terms of mobility discussed earlier are also reflected 
at the level of  individual countries, with most  EU-27  countries having  northern 
and  western  European  nations  ranked  higher  in  terms  of  destinations  for 
mobility  events,  whereas  eastern  European  nations  mostly  always  ranked 
last.  It  might  be  relevant  to  investigate  these  regional  patterns  –  through  surveys 
for instance – to better understand the dynamics at play, with the declared purpose 
of  informing  policymakers  so  that  modifications  can  be  made  to  create  a  more 
connected mobility network. 

3.3 

Impact of mobility on scientific production and citation impact metrics 

As discussed in the introduction to this policy brief, multiple studies have been performed 
regarding  the  impact  of  mobility  on  the  productivity  and  citation  impact  of  researchers, 
and  the  analyses  performed  in  the  context  of  this  policy  brief  support  these  studies’ 
findings.  Across  almost  all  countries,  publications  linked  to  internationally  mobile 
researchers  have  a  much  higher  chance  of  being  cited  in  the  scientific  literature 
compared  to  those  from  non-mobile  scholars.  Of  the  173  countries  for  which  data  for 
both  groups  could  be  compiled,  154  (89 %)  presented  with  higher  average  of  relative 
citations  (ARC)  for  their  internationally  mobile  AUIDs  compared  to  non-mobile  ones.  Of 

37 

Regions (Destination)Eastern EuropeNorthern EuropeSouthern EuropeWestern EuropeEastern EuropeBulgaria15.99.010.07.7Croatia12.811.011.37.0Czech Republic14.213.315.08.1Estonia11.35.09.29.0Hungary14.49.312.88.3Latvia6.55.06.55.3Lithuania10.75.010.09.4Poland17.09.012.88.6Romania15.08.311.29.0Slovakia13.212.713.59.0Slovenia10.711.712.88.4Northern EuropeDenmark17.34.012.88.9Finland16.64.514.28.6Sweden17.43.514.09.1Southern EuropeCyprus5.95.74.84.4Greece18.214.09.07.1Italy18.09.713.67.3Malta4.04.02.63.4Portugal16.09.011.49.0Spain18.09.313.27.7Western EuropeAustria16.011.012.010.5Belgium17.811.713.25.7France18.79.011.57.7Germany17.98.713.57.7Ireland16.710.310.87.0Luxembourg8.68.06.35.0Netherlands18.69.012.36.7Region (Source)Source 
 
 
the  few  countries  not  following  this  pattern,  most  had  limited  numbers  of  researchers 
(i.e. less than 20) for at least one of the two groupings, diminishing the reliability of the 
findings  for  these  countries;  Campbell  and  Struck  (2019)  have  shown  that  a  bare 
minimum of 500 AUIDs (ideally 1 000) are needed for highly reliable data. Findings were 
similar  in  terms  of  the  average  of  relative  CiteScores  (ARCiS)  and  top  1  %  most  cited 
papers,  which  is  not  surprising  given  that  these  indicators  are  often  correlated  at  high 
levels of aggregation (e.g. countries or regions). 

Amongst  the  selected  countries,  the  vast  majority  presented  with  higher  impact 
indicators  for  their  mobile  cohort  compared  to  their  non-mobile  cohort  (Table  13).  Only 
Lithuania, Liechtenstein, Montenegro and Iceland had some scores lower for their mobile 
cohorts, and always for indicators based on low counts of publications. Based on average 
citation  impact  (i.e.  the  ARC),  some  of  the  largest  leads  for  mobile  versus  non-mobile 
cohorts  are  observed  for  Belarus,  the  Republic  of  Moldova,  Malta,  Luxembourg,  Faroe 
Islands,  Russia,  Armenia,  Cyprus,  Latvia,  China,  Estonia,  Romania,  Serbia,  Croatia, 
Germany and Hungary. As can be noted, many of these are eastern European countries, 
and  regional  analyses  presented  later  will  clarify  this.  At  the  level  of  the  EU-27,  the 
differences  between  mobile  and  non-mobile  cohorts  range  from  20 %  to  70 %,  results 
quite like those observed at the world level. At the other end of the spectrum, amongst 
countries  with  more  than  a  few  hundred  researchers,  those  benefiting  the  least  from 
mobility  include  Sweden,  Norway,  the  United  States,  the  United  Kingdom  and 
Switzerland. Northern European countries present with an opposite pattern compared to 
eastern  European  countries,  which  will  also  be  addressed  later.  Finally,  the  presence  of 
China and the United States at both ends of the spectrum is quite telling, both countries 
being the leaders in terms of scientific output, but with researchers from China strongly 
benefiting  from  international  mobility  in  terms  of  impact,  while  researchers  from  the 
United States benefiting less from international mobility. 

The  difference  between  mobile  and  non-mobile  cohorts  in  terms  of  productivity  is  also 
notable, with mobile researchers publishing more across all countries, on average by an 
80 % margin. However, findings regarding productivity should be treated with caution, as 
the  AUIDs  are  not  perfect  and  could  under  or  over  report  output  for  some  researchers. 
That  said,  as  the  cohorts  being  compared  include  individuals  with  similar  career  length 
and are limited to individuals for which output could be tracked over 15 years, the risk of 
split  portfolios  biasing  the  overall  pattern  in  the  results  is  greatly  reduced.  Accordingly, 
the takeaway message from the above finding should be more about the fact that mobile 
cohorts  are  more  productive  than  non-mobile  ones,  and  not  so  much  about  the 
magnitude  of  these  differences.  Note  that  this  limitation  has  less  impact  for  metrics 
based  on  the  aggregation  of  normalised  scores  than  those  based  on  raw  publication 
counts; therefore, the findings for differences in impact metrics between mobile and non-
mobile researchers are certainly more reliable. 

38 

 
Table 13 

Productivity  and  citation  impact  of  mobile  versus  non-mobile 
researchers, per country 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Colour coding for impact indicators uses a gradient ranging from 
dark red for the most negative values, to white for metrics on par with the world level and dark green for the highest scores. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

For EU-27 nations, findings are similar, with the internationally mobile researchers of all 
27  nations  presenting  much  higher  scientific  impact  scores  than  their  non-mobile 
scholars. The average difference for EU-27 countries stood at 0.30 for the ARC, 0.16 for 
the ARCiS and 0.45 for the 1 % most cited. This finding suggests that mobility might be 
even  more  important  in  promoting  the  production  of  breakthrough  papers  because  it 

39 

Non-mob.Mob.Non-mob.Mob.Non-mob.Mob.Non-mob.Mob.Non-mob.Mob.Non-mob.Mob.1World408,131188,4782,915,9252,135,4287.111.30.931.230.961.140.831.422ERA153,75087,503979,917935,2406.410.70.901.250.961.170.701.403EU–27117,00062,920732,938669,7526.310.60.881.230.951.160.651.354Belgium2,5832,66315,58130,1156.011.31.111.331.081.201.041.575Bulgaria8264585,0224,0616.18.90.410.700.570.840.130.446Czech Republic2,7481,21521,33713,9177.811.50.540.820.680.880.190.607Denmark2,0531,55813,43517,5616.511.31.241.461.181.261.141.778Germany15,58912,572105,359147,5136.811.70.931.290.961.190.801.529Estonia3821832,5501,7226.79.40.751.130.921.100.301.1910Greece2,7241,64518,58618,7946.811.40.891.120.971.140.641.0511Spain16,5036,59185,99063,2785.29.60.891.240.991.200.561.2612France19,07011,672111,926110,2015.99.40.891.210.991.150.641.2913Croatia1,5314319,4403,7486.28.70.430.790.560.830.170.6714Ireland7511,1034,97612,5986.611.41.041.201.101.200.931.1715Italy20,3168,120120,57385,4585.910.50.961.261.021.180.651.3316Cyprus55773239985.913.00.861.261.061.090.580.9817Latvia214731,1296215.38.50.450.840.580.870.180.7018Lithuania7352144,7882,3506.511.00.510.660.650.740.440.3719Luxembourg1733963085.79.30.811.241.011.140.411.2620Hungary1,6451,0849,2549,3265.68.60.590.950.751.010.271.0221Malta29521585005.59.60.500.980.750.880.000.9022Netherlands6,0124,51936,73352,0806.111.51.291.491.211.321.371.8323Austria1,7261,7929,81220,0755.711.20.951.261.031.170.791.3624Poland8,6452,29769,13225,4068.011.10.500.760.650.840.160.5425Portugal2,7421,45819,29916,6067.011.40.961.171.031.130.621.0126Romania1,58570913,1258,6188.312.20.480.870.540.860.220.7827Slovenia9073166,8413,9547.512.50.711.030.760.980.341.0028Slovakia8785966,5115,7307.49.60.480.680.570.780.170.3329Finland2,6501,39316,00614,6666.010.51.081.351.141.260.801.5430Sweden4,1223,17625,09232,2506.110.21.251.381.181.251.091.5731United Kingdom16,35015,734113,825173,0557.011.01.191.361.191.251.231.6633Iceland1461506581,0144.56.81.191.251.171.221.311.2334Liechtenstein4319184.85.91.730.841.610.921.860.0035Norway2,4071,27415,84513,6016.610.71.171.321.141.191.001.4236Switzerland2,0133,42310,53835,8435.210.51.261.451.111.261.291.9637Israel2,3822,23714,70324,0876.210.80.991.321.151.340.741.4838Montenegro34162422027.112.60.620.790.810.750.360.0039Macedonia81613695414.68.90.540.670.580.660.490.6740Albania1519271121.85.90.490.590.660.980.000.2541Serbia1,1864637,7394,6106.510.00.520.910.670.850.300.8942Turkey8,4952,13062,51523,7507.411.20.660.920.690.860.310.8343Kosovo81017992.19.90.670.600.790.750.000.0944Bosnia and Herzegovina77673164564.16.80.380.420.480.480.020.0145Armenia142996817534.87.60.220.630.430.780.020.4046Azerbaijan110565375194.99.30.210.480.330.570.000.2047Belarus3552411,8452,5785.210.70.240.760.370.790.020.7048Georgia1441037148905.08.60.320.600.540.680.030.1449Rep. of Moldova50491695213.410.60.310.830.550.920.000.4950Ukraine2,2541,23813,19012,4665.910.10.240.590.350.680.060.4651Faroe Islands4717644.29.11.091.511.191.220.501.8352Tunisia9785505,4545,7605.610.50.570.710.700.800.130.3753Brazil12,2254,73269,60445,6765.79.70.740.950.810.960.300.7454Canada8,5228,84855,650100,7376.511.41.121.351.151.250.931.5455China38,87113,717463,013239,23211.917.40.691.080.730.970.471.2456India12,6515,81093,01067,4577.411.60.650.990.700.960.340.8257Japan26,81310,016170,757102,5886.410.20.701.010.861.030.440.9658South Korea7,0194,13262,10455,1748.813.40.801.080.921.080.481.0559Russia15,3283,939100,14339,6216.510.10.280.700.350.730.080.5360South Africa1,5691,11412,68813,2148.111.90.771.060.831.050.470.9561United States99,48340,168710,326464,1437.111.61.351.491.291.331.652.0162Australia6,8295,84250,74974,8217.412.81.111.351.101.220.861.55Ord.ARCiSTop 1%CountryNumber of reseachersNumber of papers per researcherNumber of papers (Frac)ARC 
 
 
 
produces a stronger effect on the scores for the 1 % most cited publications. Regarding 
the  ARC  score,  the  largest  differences  were  observed  for  Malta  (+0.48),  Luxembourg 
(+0.43),  Cyprus  (+0.40),  Latvia  (+0.39),  Estonia  (+0.39),  Romania  (+0.39),  Croatia 
(+0.36), Germany (+0.36) and Hungary (+0.36). 

Applying  the  same  analysis  to  European  regions  (Table  14)  demonstrates  that  the 
regions  benefiting  the  most  in  terms  of  increased  impact  with  mobility  are  generally 
southern and eastern Europe. Only for the share of highly cited publications (top 1 %) is 
western  Europe  gaining  slightly  more  than  eastern  Europe  (southern  being  first  in  that 
case).  However,  differences  across  regions  are  relatively  marginal,  with  countries  of  all 
four regions experiencing, on average, similar levels of increase when comparing mobile 
and non-mobile cohorts. The only notable outlier is northern Europe. For this region, the 
increases  are  noticeably  smaller.  However,  this  region  also  presents  the  highest  scores 
overall,  and  its  non-mobile  cohort  is  the  only  one  presenting  clearly  above  world  level 
scores, which might limit the scope for potential increases when mobility events occur. 

Many hypothesises can be proposed to explain what mechanisms  may be hidden behind 
these  gains  in  citations  for  mobile  researchers.  One  highly  plausible  scenario  is  a 
broadening  of  the  citation  network,  as  mobile  researchers  may  generate  attention  from 
their original network and the new one they create after migrating to a new country, thus 
increasing  the  odds  of  being  cited  compared  to  a  non-mobile  researcher,  who  can  only 
rely on a local network for uptake. 

Table 14 

Productivity  and  citation  impact  of  mobile  versus  non-mobile 
researchers, average per European region 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark red for impact metrics below the 
world level to dark green for those above the world level; a similar colouring scheme is used for differences between mobile and 
static scores, with positive differences coloured green and the negative ones coloured red. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

POLICY IMPLICATIONS 

indicators  and 
Although  mobility  appears  to  positively 
productivity  metrics,  the  largest  effects  are  measured  for  the  most  cited 
publications. These publications often being associated with research excellence, 
this  finding  could  be  quite  critical  for  the  decision-making  process  of  EU 
programmes aimed at research excellence (e.g. ERC programmes). 

impact  all 

impact 

POLICY IMPLICATIONS 

Eastern  European  nations,  which  as  discussed  earlier  present  some  of  the  lowest 
international mobility rates in the European Union, appear to be those benefiting the 
most from the international mobility of their researchers in terms of scientific impact 
when  compared  with  their  non-mobile  researcher  populations.  This  finding  is  a 
strong  argument  in  favour  of  policies  to  increase  migratory  behaviours  for 
these  countries  as  it  greatly  improves  bibliometric  impact,  at  the  same  time 
increasing  the  scientific  competitiveness  across  Europe  and  ensuring  a  more 
balanced  playing  field  in  the  European  community,  for  instance  in  competitive 
grants processes. 

40 

Papers/researcherARCARCiSHCP1%Papers/researcherARCARCiSHCP1%∆ Papers/researcher∆ ARC∆ ARCiS∆ HCP1%EU–276.30.880.940.6810.61.181.141.244.380.300.200.56Eastern Europe7.40.510.630.2110.50.800.880.633.070.290.250.42Southern Europe5.80.860.940.5810.41.171.141.164.570.310.200.58Northern Europe6.21.221.161.1110.51.341.231.454.360.120.080.34Western Europe6.21.001.020.9010.91.231.171.384.650.240.150.47RegionStatic researchersMobile researchersMobile minus static 
 
One  can  wonder  after  looking  at  the  data  above  if  there  really  is  a  causal  relationship 
between  mobility  and  impact,  or  if  the  phenomenon  observed  could  simply  be  due  to  a 
selection  bias.  In  other  words,  it  might  be  that  the  more  cited  researchers  are  exactly 
those facing fewer barriers to mobility, leading to their overrepresentation in the mobile 
cohorts  compared  to  less  favoured  colleagues  in  the  non-mobile  cohorts.  To  test  this 
hypothesis,  a  before–after  analysis  was  performed  on  the  mobile  cohorts  (Table  15). 
Because multiple mobility events can occur over the course of a career, the first mobility 
event  detected  after  the  first  five  years  following  the  first  publication  detected  was 
selected  as  the  turning  point  between  the  before  and  after  periods.  The  gap  of  5  years 
following  the  first  publication  year  was  implemented  to  avoid  detecting,  in  as  much  as 
possible, student mobility as the first mobility event. The analysis was also separated per 
year  of  mobility  event  to  compare  researchers  with  relatively  similar  before  and  after 
windows. 

As  can  be  observed,  except  for  a  few  cases,  all  cohorts  presented  higher  impact  scores 
following  mobility  compared  to  their  previous  work.  Although  this  could  simply  be  the 
result  of  researchers  becoming  more  well  known  over  time  and  not  the  single  effect  of 
mobility, it is quite striking to observe the increases for most reference years, especially 
for  the  1 %  most  cited  indicator,  again  a  potential  implication  that  mobility  plays  an 
important  role  in  the  production  of  highly  cited  papers.  However,  it  is also  important  to 
denote that, overall, mobile researchers appear to present above average impact metrics 
prior to their mobility event, which, as was noted earlier, is not the case for non-mobile 
researchers (except those from northern Europe). Therefore, it is still not possible to rule 
out  that  the  increases  observed  after  mobility  for  the  mobile  cohorts  do  not  come  from 
other  factors  than  mobility  itself.  Fully  resolving  this  attribution  issue  (i.e.  are  the 
observed  differences  truly  attributable  to  mobility?)  would  require  the  inclusion  of  a 
control group in the before–after analysis, a step which was beyond the feasible scope of 
work for this policy brief. 

Table 15 

Difference  in  impact  metrics  for  mobile  researchers,  before  and 
after  the  first  mobility  event  to  a  new  country  occurring  after  the 
first 5 years of a publishing career 

Note: 
Cohort of 2001–2003 still publishing 15 years later. Colour coding ranges from dark red for impact metrics below the 
world level to dark green for those above the world level; a similar colouring scheme is used for differences between before and 
after mobility scores, with positive differences coloured green and the negative ones coloured red. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

3.4  Mobility of researchers by scientific disciplines 

With  scientific  cultures  varying  so  much  across  scientific  disciplines,  a  valid  question  is 
whether  there  are  differences  in  mobility  patterns  across  the  disciplines.  To  tackle  this 
issue, data were prepared per field of science, following Science-Metrix’s classification of 
science,  which  encompasses  22  fields  of  science.  As  can  be  observed  at  Table  16, 
mobility  rates  in  the  EU-27  vary  greatly  across  fields,  ranging  from  21 %  in  Visual  & 
Performing Arts to a high of 54 % in Physics & Astronomy. Physics & Astronomy exhibits 
much higher mobility than any other field of science, with Biomedical Research coming in 

41 

Number of researchersPapers (FRAC)ARCARIFTop 1%Number of researchersPapers (FRAC)ARCARIFTop 1%∆ ARC∆ ARIF∆ Top 1%200532941.180.981.94322301.130.991.01-0.040.00-0.9220063,98411,3021.231.141.244,02637,3881.331.231.500.100.100.2620076,11917,4281.201.131.236,19951,9181.311.221.530.110.090.3020087,55322,8121.221.151.317,63861,1331.331.221.590.110.080.2820096,54923,5711.241.121.336,61047,8061.291.191.540.050.080.2120105,13621,1851.221.121.235,19434,5401.351.201.560.140.070.3320113,84917,6891.201.131.293,90223,6921.281.211.470.070.070.1820123,28516,6291.191.101.203,31117,4071.231.161.320.040.060.1220132,87717,2651.211.111.272,89914,1031.301.161.550.090.050.2820142,88619,2061.201.111.302,89212,4141.201.161.290.000.06-0.0220152,64318,9071.141.061.192,6528,8781.191.141.300.050.070.1220162,61017,7531.171.091.212,6036,7451.141.101.25-0.020.010.0420172,60619,2671.181.111.192,5905,3991.201.151.430.020.030.24Reference YearPrior to mobility in reference yearAfter mobility in reference yearBefore minus After 
 
 
second  at  42 %.  This  finding  should  not  be  surprising  given  the  nature  of  research 
performed in some subfields of Physics & Astronomy. Researchers in these subfields are 
often  required  to  move  across  borders  to  access  international  facilities  that  are  seldom 
found  on  a  national  scale  (e.g.  large-scale  and  expensive  facilities  such  as  particle 
accelerators,  observatories).  Other  fields  with  higher  mobility  include  Economics  & 
Business  (39 %),  Earth  &  Environmental  Sciences  (38 %),  Chemistry  (38 %), 
Information  &  Communication  Technologies  (38 %),  Biology  (35 %),  Psychology  & 
Cognitive Sciences (34 %) and Philosophy & Theology (34 %). 

Table 16 

Productivity  and  citation  impact  of  mobile  versus  non-mobile 
researchers, per field of science 

Note: 

Cohort of 2001–2003 still publishing 15 years later. Scores per field are non-weighted averages of indicators of 

individuals assigned to each field. Colour coding ranges from dark red for impact metrics below the world level to dark green for 
those above the world level; a similar colouring scheme is used for differences between mobile and non-mobile scores, with 
positive differences coloured green and the negative ones coloured red. The data are based on publications involving at least 
one EU-27 country for the 2000–2019 period. 
Source:  Prepared by Science-Metrix using the Scopus database (Elsevier) 

Fields from all five main scientific domains of the classification appear amongst the  first 
half  of  the  most  mobile  fields,  indicating  that  research  mobility  is  prevalent  across  all 
domains.  Nevertheless,  a  ranking  analysis  confirms  that  some  domains  are  more  prone 
to  mobility  events  than  others.  The  average  position  of  subfields  in  domains  can  be 
grouped  into  three  categories  based  the  extent  of  mobility  they  exhibit.  The  Natural 
Sciences and the Economic & Social Sciences rank highest for mobility, with scores of 5.8 
to 6.5  respectively (Figure 4).  They are followed by the  Health Sciences with a score  of 
10.  Lastly,  the  Applied  Sciences  and  the  Arts  &  Humanities  are  characterised  by  fewer 
mobility events with respective scores of 13.8 and 14.8. 

42 

No. of researchersAvg. Pubs (FRAC)Avg. ARCAvg. ARCiSAvg.Top1%No. of researchersAvg. Pubs (FRAC)Avg. ARCAvg. ARCiSAvg.Top1%∆ ARC∆ ARIF∆ Top1%Physics & AstronomyNatural Sci.54%6,0477.10.860.990.667,09811.81.161.121.320.300.120.66Biomedical ResearchHealth Sci.42%7,8663.80.941.040.745,6606.11.201.231.300.250.190.55Economics & BusinessEconomic & Social Sci.39%2,4257.60.800.880.521,55512.31.171.151.180.370.270.65Earth & Environmental Sci.Natural Sci.38%4,1966.10.920.990.642,5919.21.191.161.060.280.170.42ChemistryNatural Sci.38%4,2006.60.861.020.532,5699.81.121.190.980.260.170.45Inf. & Com. Tech.Applied Sci.38%5,61511.10.821.020.603,39718.81.181.241.330.360.220.72BiologyNatural Sci.35%5,0676.10.970.980.752,7239.31.281.221.290.310.240.54Psychology & Cognitive Sci.Health Sci.34%1,4047.90.670.730.4272613.40.990.970.810.330.240.39Philosophy & TheologyArts & Humanities34%3349.70.500.540.2917112.60.600.740.210.100.20-0.07Social Sci.Economic & Social Sci.33%2,0657.60.770.780.611,02811.71.150.951.360.380.160.75Enabling & Strategic Tech.Applied Sci.33%4,3527.01.001.000.912,11611.51.251.111.350.250.110.44Mathematics & StatisticsNatural Sci.32%2,43211.50.841.020.521,15316.11.071.141.040.230.110.52Comm. & Textual Stud.Arts & Humanities31%6407.00.770.680.6428710.31.210.901.280.430.210.64Clinical MedicineHealth Sci.30%43,6414.30.840.850.7118,2968.41.151.081.250.310.230.54EngineeringApplied Sci.28%6,7818.70.951.030.692,59615.01.251.181.290.300.140.60Public Hlth & Hlth ServicesHealth Sci.27%1,2465.50.870.890.5046710.81.181.031.270.310.140.77Historical StudiesArts & Humanities27%9125.90.680.700.333338.51.160.901.370.480.211.04Built Environment & DesignApplied Sci.25%4877.51.030.970.7716314.11.211.091.190.180.120.42Agri., Fisheries & ForestryApplied Sci.24%4,3546.20.971.010.671,3429.31.151.101.040.180.090.37Visual & Performing ArtsArts & Humanities21%584.70.530.520.25156.90.700.740.840.170.220.59FieldNon-mobileMobileMobile minus Non-mobile% MobileDomain 
 
 
 
Figure 4 

Average  ranking  of  scientific  fields  according  to  shares  of  mobile 
researchers, per scientific domain 

Note: 

Cohort of 2001–2003 still publishing 15 years later. The data are based on publications involving at least one EU-27 

country for the 2000–2019 period. 
Source:  Prepared by Science-Metrix using Scopus data 

In  terms  of  the  effects  of  mobility  on  scientific  performance  (i.e.  on  productivity  and 
impact  indicators),  the  groups  of  mobile  researchers  do  appear  to  perform  better  than 
the non-mobile ones across all indicators and for all fields of science: a confirmation that 
mobility  appears  to  boost  career  prospects,  regardless  of  the  discipline16.  The  largest 
increases  in  terms  of  impact  metrics  are  observed  for  the  fields  under  the  domain  of 
Economic  &  Social  Sciences,  and  this  across  all  three  impact  metrics  (+0.38  for  ARC, 
+0.22  for  ARCiS,  +0.70  for  top  1 %)  (Figure  5).  When  computing  average  rankings  of 
differences between mobile and non-mobile researchers, Economic & Social Sciences also 
ranks  first  across  all  three  indicators  (3.5  for  ARC,  7.0  for  ARIF,  4.5  for  top  1 %).  The 
indicator for which less difference across groups can be observed is the ARCiS, which is 
expected  given  that  this  is  a  journal-based  metric,  which  makes  it  less  prone  to 
fluctuations.  Health  Sciences  ranks  second  for  two  out  of  three  indicators  (ARC  and 
ARCiS), followed by Arts & Humanities (3rd for ARC and top 1 %, 2nd for ARCiS). Natural 
Sciences  rank  fourth  across  all  three  indicators,  and  Applied  Sciences  rank  last  for  all 
three also, and this for both average scores and average rankings. 

The positioning of domains is quite interesting as it appears to highlight a key difference 
between  the  ‘hard’  sciences  (i.e.  Natural  and  Applied  Sciences)  versus  the  Social 
Sciences  and  Humanities,  with  Health  Sciences  coming  in  somewhere  in  between. 
Although  it  was  demonstrated  that  mobility  appears  to  boost  bibliometric  prospects 
across  all  disciplines,  the  size  of  the  boost  appears  largest  in  the  Social  Sciences  and 
Humanities  compared  to  the  hard  sciences.  One  hypothesis  could  be  that  the  highly 
collaborative nature of these hard sciences reduces the benefits of mobility; the potential 
gains  in  uptake  from  enlarged  networks  may  have  already  been  achieved  through 
collaboration,  with  further  increases  in  the  reach  of  established  networks  through 
mobility  being  less  likely  to  impact  the  citation  uptake  of  publications.  In  comparison, 

16  Mobile  cohorts  also  perform  better  according  to  the  average  number  of  fractioned  publications 
per  researcher.  However,  because  of  the  less  robust  nature  of  this  indicator  (as  explained 
above due to the use of the AUID), it was decided that this indicator would not be prominently 
featured here. 

43 

0246810121416Natural SciencesEconomic & Social SciencesHealth SciencesApplied SciencesArts & HumanitiesAverage mobility ranking of scientific fieldsScientific domain 
 
 
                                                 
researchers  in  less  collaborative  fields  might  benefit  more  from  the  gains  obtained  by 
enlarging their network following international mobility. 

Figure 5 

Difference in impact metrics (scores and rankings) between mobile 
and non-mobile cohorts, per scientific domain after aggregation of 
data per scientific field 

Note: 

Average rankings of fields are displayed above columns, which present the increases in scores for all three metrics. 

The data are based on publications involving at least one EU-27 country for the 2000–2019 period. 
Source:  Prepared by Science-Metrix using Scopus data (Elsevier) 

To  test  for  this  hypothesis,  we  computed  the  international  collaboration  rates  per 
discipline  to  see  if  any  correlation  between  the  increases  linked  to  mobility  and 
collaboration patterns could be detected. Based on this test, not much correlation could 
be  detected,  with  an  R2  of  0.09  for  a  slightly  negative  relation  between  international 
collaboration rates and the effect of mobility on the ARC scores. However, only five data 
points are  available when doing this analysis by domain, which is not a lot. To increase 
the number of data points to test this relationship, the same analysis was prepared using 
data by field of science. At first, this analysis again did not show any correlation (Figure 
6, all fields). However, after further inspection, it became quite clear that apart from four 
outliers  greatly  skewing  the  regression,  most  other  fields  fell  in  a  pattern  showcasing  a 
decreasing  gain  in  ARC  scores  as  the  international  collaboration  rate  increased. 
Therefore, a regression analysis was performed without these four outliers, resulting in a 
negative  relationship  between  the  international  collaboration  rate  and  the  gains  in  ARC 
due to mobility (Figure 6, without fields in red, R2 of 0.62). 

44 

3.58.810.512.013.27.08.07.312.015.04.510.59.311.812.60.000.100.200.300.400.500.600.700.80Economic & SocialSciencesHealth SciencesArts & HumanitiesNatural SciencesApplied SciencesDelta mobile vs non-mobileScientific domainARCARCiSTop 1% 
 
 
Figure 6 

Correlation  between 
rates  and 
differences in ARC scores between mobile and non-mobile cohorts, 
per field of science 

international  collaboration 

Note: 
The data are based on publications involving at least one EU-27 country for the 2000–2019 period. Two regression 
analyses are presented, one in red including all 22 fields, and the other in blue excluding four outliers, shown as red circles. 
Source:  Prepared by Science-Metrix using Scopus 

4 

Conclusion 

The investigation of the international mobility of researchers (within and beyond the  EU-
27)  using  bibliometric  databases  is  a  challenging  task,  burdened  by  multiple  limitations 
as  was  discussed  in  this  brief.  Furthermore,  student  exchange  programmes  (e.g. 
Erasmus+ in Europe), dual PhD degrees (cotutelles), existing research partnerships, and 
migratory  agreements  can  all  be  factors  behind  the  observed  patterns  and  should  be 
considered  when  performing  more  in-depth  (qualitative)  investigations  of  the  presented 
data.  Nevertheless,  keeping  in  mind  these  limitations,  it  was  still  possible  to  extract 
meaningful data on mobility from a bibliographic database (Scopus). For instance, it was 
possible  to  uncover  regional  mobility  patterns  within  the  EU-27  that  are  broadly 
consistent  with  those  extracted  from  the  MORE3  survey.  These  patterns  highlighted  the 
preference  of  European  researchers  towards  countries  from  western  and  northern 
Europe,  more  so  than  for  countries  from  eastern  and  southern  Europe.  In  the  case  of 
eastern  Europe,  this  finding  holds  for  most  countries,  and  even  for  eastern  European 
countries  themselves,  which  more  frequently  favour  mobility  to  western  and  northern 
European  nations.  Multiple  factors  can  explain  this  behaviour,  including  broader 
opportunities,  fewer  barriers  (of  various  types)  and  more  attractive/prestigious 
institutions in western and northern Europe than in eastern and southern Europe. These 
factors are likely impacted by  existing mobility programmes/policies and these  could be 
altered/replaced to favour more balanced migratory flows across all of Europe. 

This brief also showed that mobile cohorts of researchers tend to be more productive and 
receive  citations  at  higher  rates  compared  to  non-mobiles  cohorts.  These  findings 
support  those  of  multiple  studies  on  the  effect  of  international  mobility  on  citation 
indicators (Sugimoto et al., 2017). This study’s results indicate that, within the EU-27, all 
publications  associated  with  individuals  having  moved  after  the  first  five  years  of  their 
publishing  career  were  associated  with  higher  citation  impact  than  those  linked  to  non-
mobile  researchers.  This  held  true  in  the  aggregate  as  well  as  by  country  and  impact 

45 

Visual & Performing ArtsPhilosophy & TheologyCommunication & Textual StudiesHistorical StudiesSocial SciencesBuilt Environment & DesignClinical MedicineEngineeringInformation & Communication TechnologiesPublic Health & Health ServicesAgriculture, Fisheries & ForestryEconomics & BusinessPsychology & Cognitive SciencesEnabling & Strategic TechnologiesChemistryMathematics & StatisticsBiomedical ResearchBiologyEarth & Environmental SciencesPhysics & Astronomy0.000.100.200.300.400.500.600%10%20%30%40%50%60%∆ ARC (mobile vs non-mobile)International collaboration rateWithout fieldsin redy= -0.45x + 0.48R2= 0.62All fieldsy= -0.03x + 0.30R2= 0.001 
 
 
indicator  (three  different  impact  indicators  were  used),  except  for  the  share  of  highly 
cited publications (top 1 %) for Lithuania17. The mechanisms behind these patterns have 
not  been  studied  in  detail,  but  it  appears  plausible  that  mobility  would  contribute  to 
broadening  an  individual’s  network,  thereby  increasing  visibility  by  reaching  possibly 
disconnected communities who will each cite that researcher’s work. 

Analyses by field of science were also prepared, and they highlighted that although there 
are differences across fields of  science in terms of mobility – both regarding prevalence 
of mobility and its effect on the citation impact of resulting publications – mobility can be 
an  important  factor  in  the  career  of  researchers,  regardless  of  their  field  of  research. 
Higher mobility rates were detected in Physics & Astronomy and in Biomedical Research, 
and it was shown that of the five main domains of science, mobility was most prevalent 
in  the  Natural  Sciences,  the  Economic  &  Social  Sciences  and  the  Health  Sciences.  In 
terms  of  comparison  between  mobile  and  non-mobile  groups,  an  inverse  relationship 
between  the  prevalence  of  international  collaboration  in  a  field  and  the  difference  in 
citation advantage of mobile researchers was detected; that is, the more internationally 
collaborative  a  field  is,  the  less  advantage  mobile  researchers  get  in  terms  of  citations 
compared to their non-mobile colleagues, at least in the EU-27. 

In conclusion, mobility appears to be an important mechanism in research,  and appears 
to  boost  the  bibliometric  indicators  of  those  who  follow  this  path.  In  a  policy  context, 
these  findings  may  have  important  consequences,  as  they  may  give  rise  to  questions 
regarding  how  research  is  being  performed  and  funded.  Given  that  mobility  appears  to 
increase  the  odds  of  being  cited,  should  the  migratory  status  of  researchers  be 
considered  when  competitive  grants  are  being  awarded?  Furthermore,  assuming  that 
having  more  citations  is  indeed  better  and  leads  to  better  science,  should  policies 
promoting mobility be more prevalent in the EU? Again, studies have demonstrated that 
both  the  original  and  destination  countries  reap  benefits  from  mobility  in  research,  and 
fostering  higher  mobility,  especially  within  the  EU  itself,  could  strengthen  the  European 
research community, to the benefit of all its Member States. Given that the most popular 
destinations of EU-27 researchers – the United States and the United Kingdom – are not 
part of the European Union, it appears relevant to question whether this may lead to lost 
benefits  for  Europe  –  although,  of  course,  one  would  need  to  account  for  the  citation 
benefit  of  partnering  with  the  United  States  and  the  United  Kingdom  to  answer  this 
question.  It  is  also  relevant  to  question  how  policymakers  could  act  to  foster  higher 
mobility  between  the  27  Member  States,  without  necessarily  downgrading  the  extent  of 
partnerships with external partners that excel in research. 

17 Because of the low level of output associated with Lithuania, its score on this indicator is prone 
to fluctuations and it is not surprising that it might not follow the general trend observed across 
all other countries. 

46 

 
 
 
                                                 
REFERENCES 

Aman, V. (2018). Does the Scopus author ID suffice to track scientific international 

mobility? A case study based on Leibniz laureates. Scientometrics, 117(2), 705–
720. https://doi.org/10.1007/s11192-018-2895-3 

Campbell, D., Roberge, G., Ventimiglia, A., Labrosse, I., Lefebvre, C., Picard-Aitken, M., 
… Archambault, É. (2015). Analysis of Bibliometric Indicators for European 
Policies, 2000–2013. Brussels, Belgium: Prepared by Science-Metrix for the 
Directorate-General for Research and Innovation of the European Commission. 
Retrieved from http://ec.europa.eu/research/innovation-
union/pdf/bibliometric_indicators_for_european_policies.pdf#view=fit&pagemode
=none 

Campbell, D., & Struck, B. (2019). Reliability of Scopus author identifiers (AUIDs) for 

research evaluation purposes at different scales. In 17th International Conference 
of the International Society for Scientometrics and Informetrics (ISSI), 2-5 
September 2019. 

Chinchilla-Rodríguez, Z., Miao, L., Murray, D., Robinson-García, N., Costas, R., & 

Sugimoto, C. R. (2018). A Global Comparison of Scientific Mobility and 
Collaboration According to National Scientific Capacities. Frontiers in Research 
Metrics and Analytics, 3(May), 1–14. https://doi.org/10.3389/frma.2018.00017 

European Commission. (2019). European Research Area Progress Report 2018. 

https://op.europa.eu/en/web/eu-law-and-publications/publication-detail/-
/publication/5641328c-33f8-11e9-8d04-01aa75ed71a1 

Hristov, H., Slavcheva, M., Jonkers, K., & Szkuta, K. (2016). Intersectoral mobility and 
knowledge transfer. Preliminary evidence of the impact of intersectoral mobility 
policy instruments. European Commission. Retrieved from 
http://publications.jrc.ec.europa.eu/repository/bitstream/JRC102534/jrc102534_i
sm_report_final.pdf 

Moed, H. F., Aisati, M., & Plume, A. (2013). Studying scientific migration in Scopus. 

Scientometrics, 94(3), 929–942. https://doi.org/10.1007/s11192-012-0783-9 

Morano-Foadi, S. (2005). Scientific mobility, career progression, and excellence in the 

European Research Area. International Migration, 43(5), 133–162. 
https://doi.org/10.1111/j.1468-2435.2005.00344.x 

OECD. (2016). Policy profile: System innovation. 

Roberge, G., & Campbell, D. (2012). Canadian researchers migration analysis based on 

Scopus Author IDs (pp. 884–885). Retrieved from 
http://www.sticonference.org/Proceedings/vol2/Roberge_Canadian_884.pdf 

Robinson-García, N., Sugimoto, C. R., Murray, D., Yegros-Yegros, A., Larivière, V., & 

Costas, R. (2018). Scientific mobility indicators in practice: International mobility 
profiles at the country level. Profesional de La Informacion, 27(3), 511–520. 
https://doi.org/10.3145/epi.2018.may.05 

Rodrigues, M. L., Nimrichter, L., & Cordero, R. J. B. (2016). The benefits of scientific 

mobility and international collaboration. FEMS Microbiology Letters, 363(21), 1–5. 
https://doi.org/10.1093/femsle/fnw247 

Science Europe, & Elsevier’s SciVal Analytics. (2013). Comparative benchmarking of 

European and US research collaboration and researcher mobility. A report 
prepared in collaboration between Science Europe and Elsevier’s SciVal Analytics. 
Science Europe. 

Sugimoto, C. R., Robinson-Garcia, N., & Costas, R. (2016). Towards a global scientific 

brain: Indicators of researcher mobility using co-affiliation data, (December). 
Retrieved from http://arxiv.org/abs/1609.06499 

47 

 
Sugimoto, C. R., Robinson-Garcia, N., Murray, D. S., Yegros-Yegros, A., Costas, R., & 

Larivière, V. (2017). Scientists have most impact when they’re free to move. 
Nature, 550(7674), 29–31. https://doi.org/10.1038/550029a 

48 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Getting in touch with the EU 

IN PERSON 
All over the European Union there are hundreds of Europe Direct information centres. 
You can find the address of the centre nearest you at: https://europa.eu/european-union/contact_en 

ON THE PHONE OR BY EMAIL 
Europe Direct is a service that answers your questions about the European Union. 
You can contact this service: 
– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 
– at the following standard number: +32 22999696, or 
– by email via: https://europa.eu/european-union/contact_en 

Finding information about the EU 

ONLINE 
Information about the European Union in all the official languages of the EU is available on the Europa 
website at: https://europa.eu/european-union/index_en 

EU PUBLICATIONS 
You can download or order free and priced EU publications from: 
https://op.europa.eu/en/publications. Multiple copies of free publications may be obtained by 
contacting Europe Direct or your local information centre (see https://europa.eu/european-
union/contact_en). 

EU LAW AND RELATED DOCUMENTS 
For access to legal information from the EU, including all EU law since 1952 in all the official language 
versions, go to EUR-Lex at: http://eur-lex.europa.eu 

OPEN DATA FROM THE EU 
The EU Open Data Portal (http://data.europa.eu/euodp/en) provides access to datasets from the EU. 
Data can be downloaded and reused for free, for both commercial and non-commercial purposes. 

 
 
 
 
 
 
The  Key  indicators  for  Research  and  Innovation  project  was  a  three-year  project 
designed  to  support  the  development  and  implementation  of  the  Commission's  R&I 
policies with evidence at the EU, country and regional levels. As a result, 11 publications 
reflecting the work of the project were produced: 

-  The  final  study  report  makes  a  summary  of  the  work  done  for  each  of  the  six  work 

packages of the project; 

-  The  final  study  approach  report  covers  the  methodology  applied  to  collect  and  treat 

the data and the computation of the indicators; 

-  The ten policy briefs provide an in-depth analysis of different research questions with 
the  aim  of  formulating  policy  recommendations  in  support  of  evidence-based 
policymaking. 

Studies and reports