RESEARCH ARTICLE

Analyzing scientific mobility and collaboration in
the Middle East and North Africa

Jamal El-Ouahi1,2

, Nicolas Robinson-García3

, and Rodrigo Costas1,4

1Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, Netherlands
2Clarivate Analytics, Dubai Internet City, Dubai, United Arab Emirates
3Delft Institute of Applied Mathematics (DIAM), TU Delft, Netherlands
4DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy,
Stellenbosch University, Stellenbosch, South Africa

Keywords: collaboration, globalization, international mobility, Middle East, North Africa, research
policy, scientometrics indicators

ABSTRACT

This study investigates the scientific mobility and international collaboration networks in the
Middle East and North Africa (MENA) region between 2008 E 2017. By using affiliation
metadata available in scientific publications, we analyze international scientific mobility flows
and collaboration linkages. Three complementary approaches allow us to obtain a detailed
characterization of scientific mobility. Primo, we uncover the main destinations and origins
of mobile scholars for each country. Results reveal geographical, cultural and historical
proximities. Cooperation programs also contribute to explain some of the observed flows.
Secondo, we use the academic age. The average academic age of migrant scholars in MENA
was about 12.4 years. The academic age group 6–10 years is the most common for both
emigrant and immigrant scholars. Immigrants are relatively younger than emigrants, except for
Iran, Palestine, Lebanon, and Turkey. Scholars who migrated to Gulf Cooperation Council
countries, Jordan, and Morocco were on average younger than emigrants by 1.5 years from the
same countries. Third, we analyze gender differences. We observe a clear gender gap: Male
scholars represent the largest group of migrants in MENA. We conclude by discussing the
policy relevance of the scientific mobility and collaboration aspects.

a n o p e n a c c e s s

j o u r n a l

Citation: El-Ouahi, J., Robinson-García,
N., & Costas, R. (2021). Analyzing
scientific mobility and collaboration in
the Middle East and North Africa.
Quantitative Science Studies, 2(3),
1023–1047. https://doi.org/10.1162/qss
_a_00149

DOI:
https://doi.org/10.1162/qss_a_00149

Supporting Information:
https://doi.org/10.1162/qss_a_00149

Received: 17 settembre 2020
Accepted: 19 Luglio 2021

Corresponding Author:
Jamal El-Ouahi
j.el.ouahi@cwts.leidenuniv.nl

Handling Editor:
Vincent Larivière

INTRODUCTION

1.
In the words of the physicist Julius Robert Oppeinheimer, “the best way to send information is
to wrap it up in a person” (Oppenheimer, 1948). The mobility of highly proficient individuals
is a key mechanism by which institutions acquire knowledge and stimulate creativity and inno-
vation (Dokko & Rosenkopf, 2010; Mawdsley & Somaya, 2016; Palomeras & Melero, 2010;
Singh & Agrawal, 2011; Slavova, Fosfuri et al., 2015). They can serve as knowledge transmitters
by transferring their prior knowledge to their receiving locations (Dokko, Wilk, & Rothbard,
2009; Jaffe, Trajtenberg, & Henderson, 1993). Additionally, they can intermediate connections
with specialists known in prior locations (Breschi & Lissoni, 2009; Miguélez & Moreno, 2013;
Singh, 2005). Scientists are no exception. Mobility has been described as a key aspect to
improve scientific research (OECD, 2008; Scellato, Franzoni, & Stephan, 2015). Allo stesso modo,
international collaboration promotes the production of high-quality knowledge (Wilsdon,

Copyright: © 2021 Jamal El-Ouahi,
Nicolas Robinson-García,
and Rodrigo Costas.
Pubblicato sotto Creative Commons
Attribuzione 4.0 Internazionale
(CC BY 4.0) licenza.

The MIT Press

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Scientific mobility and collaboration in the Middle East and North Africa

2011) and is indispensable to solve complex scientific problems (Sonnenwald, 2007).
Scholars would usually produce higher-impact research when moving and collaborating inter-
nationally (Franceschet & Costantini, 2010; Gazni, Sugimoto, & Didegah, 2012; Glanzel, 2001;
Sugimoto, Robinson-García et al., 2017; Van Raan, 1998).

Several authors have addressed scientific mobility from a sociological and an economical
perspective (Baldwin, 1970; Beine, Docquier, & Rapoport, 2008; Boulding, 1966; Di Maria &
Stryszowski, 2009; Hayek, 1945; Johnson, 1965; Kidd, 1965; Mountford, 1997). The most
dominant concept of “brain drain” appeared in the migration literature in the 1960s. Primo, Esso
focused on the losses of highly skilled professionals from Europe, mainly the United Kingdom,
to the United States, described as the “world’s largest skills magnet” by Lowell (2003). It has
been shown that the “brain drain” had damaging effects for example in Eastern or Southern
European countries (Ackers, 2005; Glytsos, 2010; Morano Foadi, 2006), or in Latin America
(Times Higher Education, 2017) and Africa (OECD, 2015). Multiple innovative policy strate-
gies even aimed at improving the “brain drain” issue in regions such as in Asia (Krishna &
Khadria, 1997; Song, 1997; Zweig, 1997). Tuttavia, there is a clear uncertainty about the
impact of international flows in academia. Other labels such as “brain gain,” and “brain cir-
culation” for countries or “brain transformation” for individuals are also commonly used.
Cañibano and Woolley (2015) revised in detail the concept of “brain drain” and its historical
evolution. They also discussed the framework of “diaspora knowledge network” introduced by
Meyer (2001). Cañibano and Woolley (2015) concluded that these two frameworks, although
useful, ignore structural and context-dependent factors that affect mobility and its effects. Scott
(2015) argues that labels currently used to discuss scientific mobility are out-of-date. He uses
two broad frameworks to describe and analyze the mobility of academic staff. “Hegemonic
internationalization” is the dominant framework which focuses on migration flows from the
“periphery” to an evolving “core.” The second framework, labeled as “fluid globalization,"
focuses on the emergence of global communities, social movements and issues of develop-
ment. Scott (2015) concludes that the “fluid globalization” framework may be more useful to
understand the trends in scientific mobility. He describes the scientific mobility as a “spectrum,"
from the deeply rooted to the highly mobile scientists, with most scholars standing in the middle
of that spectrum. But his frameworks still focus on mobility flows from a “periphery” to a single
“core,” dominated by the West and increasingly evolving towards the East.

From a science policy perspective, collaboration and mobility studies improve the under-
standing of policy-makers and research managers when assessing the scientific output of their
countries or their organizations in a wider terrain of globalization. In the context of global
mobility, nation states have developed their immigration policies to attract distinguished
scholars and young researchers. On the one hand, collaboration and mobility are used as a
means to integrate global scientific networks (Nerad, 2010). D'altra parte, collaboration
and mobility are a means to internationalize national science systems. International mobility
and collaboration are indeed perceived as two sides of internationalization, with the former
being a trigger of the latter (Kato & Ando, 2017). While some countries depend on foreign-
born scholars to preserve their scientific status (Levin & Stephan, 1999; Stephan & Levin,
2001), other countries consider mobility as a means to improve their national scientific capac-
ities (Ackers, 2008), or to be considered as scientifically advanced countries (Kato & Ando,
2017). These cases are well positioned with the concept of internationalization perceived
as the set of policies, programs, and practices undertaken by academic systems, istituzioni,
and individuals “to cope with globalization and to reap its benefits” (Altbach & Knight, 2007).
Existing research provides evidence of positive effects of international mobility on the careers
of scientists with the broadening of their networks (Netz, Hampel, & Aman, 2020).

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Scientific mobility and collaboration in the Middle East and North Africa

It is only recently that bibliometric methods have offered a plausible solution to macrolevel
analyses of international mobility (Laudel, 2003; Sugimoto, Robinson-García, & Costas, 2016).
Computational advancements, and especially the development of author name disambiguation
algorithms, now allow tracking of scientists’ mobility patterns based on changes in their affilia-
tions in publications over time. The first macro studies on mobility using bibliometric methods
were proposed by Henk Moed and colleagues (Moed, Aisati, & Plume, 2012; Moed & Halevi,
2014). These studies were mostly characterized by a brain drain/gain perspective, in which fea-
tures such as multiple affiliation and cases of simultaneous affiliations were not specifically con-
sidered. To tackle this issue, Robinson-García, Sugimoto et al. (2019) proposed a taxonomy of
mobility types based on the persistence in time of scientists’ linkage to countries. They distin-
guished between migrants and travelers. Migrants are characterized by having a cutting point at
which they stop being affiliated to a country. Travelers maintain their linkage to a country, while
adding other international affiliations (Ackers, 2005; Chinchilla-Rodríguez, Miao et al., 2018;
Laudel, 2003; Robinson-García, Sugimoto et al., 2018; Robinson-García et al., 2019; Sugimoto
et al., 2016, 2017). Among other advantages, bibliometric tracking of scientific mobility allows
gaining access to mobility data in regions in which there is a lack of other sources of mobility
informazione (per esempio., surveys), as well as allowing diachronic analyses (Malakhov & Erkina, 2020;
Miranda-González, Aref et al., 2020; Yurevich, Erkina et al., 2020). Specific studies in different
regions of the world and selected countries have been performed to better understand how they
are integrated in the global network and how globalization affects specific geographical regions
(Bernard, Bernela, & Ferru, 2021; Subbotin & Aref, 2020; Wang, Hooi et al., 2019UN; Wang, Luo
et al., 2019B; Zhao, Aref et al., 2021). Other studies have been conducted to develop individual-
level migration data and key features of mobile researchers including patterns of migration by
academic age, disciplines, and gender (Aref, Zagheni, & West, 2019; Zhao et al., 2021). Such
studies contribute to a better understanding of scientific mobility by policy-makers and research
managers in their countries or their institutions.

This paper contributes to Scott’s frameworks on “hegemonic internationalization” and “fluid
globalization,” where we focus on regional mobility linkages to analyze the scientific mobility
phenomenon in the Middle East and North Africa (MENA) region. MENA countries have made
considerable investments in science and technology capacity to promote research and innova-
zione (Schmoch, Fardoun, & Mashat, 2016; Shin, Lee, & Kim, 2012; Siddiqi, Stoppani et al., 2016).
Such investments specifically target the internationalization of their domestic research. For this,
attraction of foreign talent is a key element. Some outcomes of such investment are already vis-
ible, with some of these countries experiencing a recent growth in scientific production
(Cavacini, 2016; Gul, Nisa et al., 2015; Hassan Al Marzouqi, Alameddine et al., 2019; Sarwar
& Hassan, 2015). Several international experts’ groups have regularly met to discuss the inter-
national migration and developments in some of the MENA countries (International Labour
Office, 2009; League of Arab States, 2009; United Nations, 2002–2018). Few other reports
and studies have also examined the migration of highly skilled workers in this specific region
(Fargues, 2006; Özden, 2006; UNESCO, 2015). The “brain drain” framework is the main per-
spective in all these papers. Fargues (2006) and Özden (2006) also mentioned the poor quality or
the lack of reliable migration data as well as the need for policies to enhance the benefits of mi-
gration for the development and the integration of the region. We also address the lack of reliable
dati. Özden (2006) presented the extent of the so-called “brain drain” from MENA by using the
data set prepared by Docquier and Marfouk (2005). Tuttavia, this data is limited to migration
flows to OECD countries and ignores major destinations and origins for scholars in MENA.

In contrast to assuming that MENA countries suffer from a brain drain, in a more recent
bibliometric study (Robinson-García et al., 2019) we observed that countries such as Qatar,

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Scientific mobility and collaboration in the Middle East and North Africa

Iraq, Saudi Arabia, and the United Arab Emirates were world leaders in terms of relative attrac-
tion of foreign scientists. Clearly, a more nuanced theoretical perspective is needed to under-
stand mobility in the MENA region.

in questo documento, we focus on the MENA region, aiming at better understanding the scientific
mobility and collaboration in this region of the world. Specifically, we provide new ways to
answer the questions that motivated earlier studies by pursuing the following research
objectives:

1. To profile countries in the MENA region based on their mobile scientific workforce.
2. To identify the main countries with which the MENA region interacts, distinguishing

between origin and destinations of mobile scholars.

3. To characterize the mobile scientific workforce in MENA countries based on their
personal features. We focus specifically on their academic age (Nane, Larivière, &
Costas, 2017) and gender.

4. To compare mobility and collaboration networks at the regional level.

The results of this study are expected to inform science policy-makers in the MENA region
by providing them with additional evidence about the mobility patterns in the region, così
providing better and more contextualized interpretations to the policies regarding the mobility
of the scholarly workforce in the MENA countries. Inoltre, the results deployed in this study
can also work as supporting evidence for policy-makers from other countries and regions (per esempio.,
Africa, EU, North America, Latin America) to understand the development of the MENA region
regarding the internationalization of its workforce and its outcomes.

2. DATA AND METHODS

2.1. Data Collection

In this study we use bibliometric data to track scientific mobility by identifying affiliation
changes over time. We base our analyses on three Web of Science Core Collection indices
(the Science Citation Index Expanded, the Social Sciences Citation Index, and the Arts &
Humanities Citation Index). We rely on an author name disambiguation algorithm to identify
the complete publication history of scientists. Several algorithms have been proposed to
perform such disambiguation (Backes, 2018; Caron & van Eck, 2014; Cota, Gonçalves,
& Laender, 2007; D’Angelo & Van Eck, 2020; Mihaljevic(cid:1) & Santamaría, 2021; Schulz,
Mazloumian et al., 2014; Torvik & Smalheiser, 2009). The most promising one is that by
D’Angelo and Van Eck (2020), which filters and merges the results of the algorithm by
Caron and van Eck (2014), relying on an external source of information. This method achieves
a precision of 96% and a recall of 96%. Tuttavia, the existence of an external database is
“crucial for the applicability of [their] approach” (D’Angelo & Van Eck, 2020, P. 904). Questo
study presents a regional analysis of 22 countries, making it difficult to obtain such external
fonti. Hence, we use the approach proposed by Caron and van Eck (2014). Although with
lower precision (95%) and recall (90%), this algorithm is the unsupervised method producing
the most promising results, as shown by Tekles and Bornmann (2020).

We focus on the 2008–2017 period, as it is only possible to track affiliation changes in Web
of Science since 2008, when authors and their affiliations started to be linked and recorded in
the database. We identify 22.6 million disambiguated authors who have published around
18.2 million distinct papers irrespective of the document types.

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Scientific mobility and collaboration in the Middle East and North Africa

Indicator
Academic
origin

Tavolo 1.

Indicators, definitions and calculations

Calculation

Researcher’s country affiliation on his or her first publication (Robinson-García,

Cañibano et al., 2016; Sugimoto et al., 2017).

Academic age

Age of the researcher’s first publication (Nane et al., 2017).

Type
Demography

Gender

Gender of an author, inferred by an algorithm based on three different APIs: Genderize.io,
Gender-guesser, and Gender API, which consider the first name of the author and the
suspected country of origin.

Mobility type

Taxonomy developed by Robinson-García et al. (2019) based on changes of author’s affiliations.

Mobility

Average degree

The average degree measures the spread of influence across the network. Sum of all degrees

Network

divided by the number of nodes in a network (Hanneman & Riddle, 2005).

Diameter

Maximum of distances between a pair of nodes in a network (De Nooy, Mrvar, & Batagelj, 2018).

Clustering

coefficient

Density

Proportion between the number of edges in the neighborhood of a node and the number of

potential edges in an entire weighted network (Barabási, Jeong et al., 2002; Watts &
Strogatz, 1998).

Degree of cohesion that exists among the vertices, determining whether a weighted network has
a thin or thick consistency. Ratio of actual connections by number of potential connections
(Wasserman & Faust, 1994).

As per the World Bank (2019B), the MENA region is composed of 19 countries: Algeria,
Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman,
Palestine, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, and Yemen. We also
included Afghanistan, Pakistan, and Turkey as being commonly included in the MENA region
(also often called Middle East, North Africa, Afghanistan, and Pakistan (MENAP1) and Middle
East, North Africa and Turkey (MENAT2)). The data set under study was comprised of
1,468,939 disambiguated authors who have contributed to 963,741 publications.

2.2.

Indicators

Tavolo 1 lists the indicators we have used in our study as well as their definitions, how they are
computed, and the types of data.

Although our study is limited to the 2008–2017 period, the academic age of a researcher is
calculated based on his or her first publication, which can of course be published before 2008.
In this study, we use the taxonomy developed by Robinson-García et al. (2019) which estab-
lishes the following mobility types:

1. not mobile: researchers who are always affiliated to the same country (per esempio., country A);
2. migrants: those who at one point leave their country of first publication (per esempio., they start
in country A and are affiliated later with country B, and without further ties with coun-
try A). In this study we expand this typology by distinguishing at the country level
between emigrants (for country A in our example before) and immigrants (for country
B in our example before);

1 MENAP: https://www.imf.org/en/ Publications/ REO/MECA/Issues/2019/10/19/reo-menap-cca-1019#Sum
2 MENAT: https://en.wikipedia.org/wiki/ MENA

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Scientific mobility and collaboration in the Middle East and North Africa

Tavolo 2.

Researchers by mobility type in MENA (2008–2017)

Mobility type
Not Mobile

Mobile

Migrants

Traveler (directional)

Traveler(nondirectional)

Insufficient information

Tutto

Total share
84.7%

12.1%

3.3%

5.7%

3.1%

3.2%

100%

Mobility share

100%

27%

47%

26%

–

Total
1,244,858

177,027

48,134

83,323

45,570

47,054

1,468,939

3.

4.

travelers (directional): those who change countries but are linked to their country of
origin throughout the study period (per esempio., a researcher going from country A to A and
B). We expand this typology to outgoing and incoming travelers (in the example before,
A is the outgoing country, and B is the incoming country); E
travelers (nondirectional): researchers who are always linked to the same set of countries
and hence we cannot establish the direction of movement (per esempio., researchers affiliated to
countries A and B in all their publications).

As a result of the above, we apply five final typologies of mobility to characterize the workforce

of each country: not mobile, emigrant, immigrant, outgoing travelers and incoming travelers.

Tavolo 2 shows the number of researchers for each mobility type during the 2008–2017 period
for the whole MENA region. Most researchers (84.7%) have not shown any sign of international
mobility, whereas around 12% Avere. Mobile scholars are mainly Travelers (directional), repre-
senting 5.7% of the researchers under study. Migrant is the second most common type of
mobility in MENA (3.3%), followed closely by Traveler (nondirectional) (3.1%).

As noted by Robinson-García et al. (2019), the share of researchers by mobility type in-
creases as the number of publications by researcher increases. Tuttavia, the same authors
observed an exception for nondirectional travelers: More than half of the researchers assigned
to this typology have published one or two papers. This led us to consider that this group may
be affected by the potential errors derived from the disambiguation algorithm used in our
study, which tends to split authors when the probability of publications belonging to the same
author is low. To avoid this limitation, in this study, we exclude nondirectional travelers from
our analyses. It is worth noting that 47,054 authors do not hold enough information either from
the author disambiguation algorithm or from the mobility taxonomy, which requires the pub-
lication of at least two publications to track the change of affiliations. These scientists were
also excluded from our analyses.

Figura 1 shows the number of mobile researchers per country along with their mobility
type. Considering the relatively low numbers of mobile authors in Djibouti, Bahrain,
Afghanistan, Palestine, and Yemen, these five countries were excluded from our data set.

To infer a gender for authors, we follow the same strategy as that employed in the 2019
edition of the Leiden Ranking3. We infer a gender based on the researcher’s first name and

3 https://www.leidenranking.com/information/indicators#gender-indicators

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Scientific mobility and collaboration in the Middle East and North Africa

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Figura 1. Number of mobile directional scholars per country and mobility type (2008–2017).

their suspected country of origin. If no gender can be inferred, it is then considered unknown.
The process is the following. Primo, for each author, one or more countries of origin are deter-
mined. In a publication, each author is linked to an affiliation, which includes an address with a
country. If the country of the author in his or her first publication is the same as the country the
author is most often associated with in his or her set of papers, we then consider this country as
the author’s country of origin. Otherwise, we consider there is not enough evidence to define a
single country of origin. All countries to which an author is linked are considered to be countries
of origin. Then we used three tools to infer a gender: Gender API (gender-api.com), Gender
Guesser (pypi.org/project/gender-guesser), and Genderize.io (genderize.io). It has been shown
that Gender API performs better, as evaluated in a previous study (Santamaría & Mihaljevic(cid:1),
2018). The first name of the author combined with the country of origin were provided as inputs
to these tools. This approach was applied to 24.6 million authors in the Web of Science with a
confidence level of 90%. For 44% of them, a male gender was inferred. A female gender was
inferred for 25% of the authors. For the remaining 32% of the authors, no gender could be in-
ferred, and these were labeled as N/A. We should keep in mind that these shares vary from coun-
try to country, as shown in Appendix A in the Supplementary Information. A male gender was
inferred for 57% of the disambiguated authors affiliated to a MENA country during the study
period. For 33% of them, a female gender was inferred, while no gender could be inferred for
the remaining 10%.

2.3. Network Analysis

We constructed coauthorship networks as a proxy to examine collaboration patterns within
the scientific community in MENA. These networks are presented at the national level, con

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Scientific mobility and collaboration in the Middle East and North Africa

countries represented by nodes and the number of coauthored papers by vertices. Two coun-
tries are connected by an edge when at least one scholar from country A has coauthored a
paper with a scientist from country B.

In the case of the mobility networks, the methodology varies slightly. Here, edges represent
the number of researchers who have been affiliated at any given point in time within the study
period between countries A and B. Two countries are connected by an edge when at least one
scholar has a mobility event from a country to another. Network visualizations were created
using VOSviewer (van Eck & Waltman, 2009).

2.4. Limitations of Bibliometric Approaches for Mobility

It is important to acknowledge that there are several limitations to the methods we used. Primo,
our methods rely mainly on tracking the changes in authors’ affiliations to measure the mobil-
ità. Così, researchers with a low number of papers would most probably be underrepresented
(Abramo, D’Angelo, & Solazzi, 2011). Secondo, certain types of mobility events, such as short-
term stays, are not necessarily translated into publications. A third limitation is due to the cov-
erage in Web of Science, thus limiting our study to publications in indexed journals. Fourthly,
the author name disambiguation algorithm we used (Caron & van Eck, 2014) uses rule-based
scoring and clustering based on bibliographic information such as author name, email address,
affiliation, publication source, and citation information. The method used is conservative, as it
values precision over recall. If there is not enough evidence to group publications together,
they will be grouped in separate clusters. Errors in publication coupling might occur for several
reasons. Per esempio, an author with a high frequency of affiliation change might be clustered
into several different “authors” by the algorithm. To a lesser extent, this problem might also
apply to authors who did not change their affiliations. For many authors, the algorithm splits
up the publications under multiple author identities. Typically, there is one dominant identity
that covers most of the papers and few separate identities that include only one or two pub-
lications. These are considered as artefacts of the disambiguation of the algorithm and are ex-
cluded from our study. Nevertheless, with these limitations in mind, Sugimoto et al. (2017)
discussed to some extent the validity of the approach used to identify international mobility
by comparing the mobility algorithms with affiliation data recorded in the Open Researchers
and Contributor ID (ORCID) public data file, finding that about 63% of researchers mobile in
ORCID were also identified by bibliometric means, supporting the relevance of bibliometrics
but also highlighting the relative conservative perspective of the bibliometric approach. A
assess the accuracy of the approach for disambiguating authors, we compared our data set
with the 2020 ORCID public data file that we consider as a reference data set. In this file,
the registered researchers are uniquely associated with their scientific oeuvre. We used this
information to verify the accuracy of the disambiguation algorithm. We first matched the pub-
lications of our data set with those available in ORCID by using unique identifiers, such as the
DOI, the Web of Science Accession Number, or the PubMed ID. For the matched records, we
examined the authors disambiguated by the algorithm with the authors in ORCID by analyzing
the last name and the first forename initial. If the name strings from both sources match, we
assume they refer to the same author. We also use the email address as additional information
to match the author names. A total of 6,459 disambiguated authors were associated with an
ORCID. Tavolo 3 reports the number and the percentage of correct and incorrect (cioè., Rif-
searchers with more than one disambiguated cluster) disambiguation. Of the disambiguated
authors, 91.1% were correctly matched with one ORCID, while 8.9% of authors in the ORCID
public file were split into multiple disambiguated authors by the algorithm.

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Tavolo 3.

Statistics for our approach to author disambiguation versus ORCID records

ORCID

5,884

(91.1%)

575

(8.9%)

Disambiguation algorithm

Correct

Incorrect

Finalmente, the algorithm we used to infer the gender of authors is of course not perfect and we
should keep these limitations in mind when analyzing the results. Overall, the limitations dis-
cussed above indicate that we are most likely underestimating the true mobility that we are
measuring, and therefore we are taking a quite conservative approach, in which we expect a
high precision in what is captured (cioè., the mobility events are correct in the framework of this
paper), but not all mobility events can always be properly identified.

3. RESULTS

In this section, we present the main findings of the study. Primo, we offer an overview on the
number of identified scientists by country as well as the proportion they represent by mobility
types at the regional level. Prossimo, the mobility profiles of each country in MENA are presented,
followed by an analysis of the mobility flows. Then, we focus on the gender and the academic
age of mobile scholars. Finalmente, we compare the mobility and the collaboration networks.

3.1. General Results and Country Profiles

In Figure 2, we summarize the number of disambiguated researchers per country as well as the
papers published during the study period. Authors affiliated to Iranian institutions show the
highest rate of publications per researcher, followed by scholars in Turkey and Tunisia.

We develop country profiles of the MENA region based on the mobile scientific workforce
identified. In Figure 3, we report the in-migration and the out-migration per country. Saudi
Arabia, United Arab Emirates, Qatar, Kuwait, and Oman, part of the Gulf Cooperation
Council (GCC), all have higher rates of incoming scholars (~79%) than outgoing. These five

Figura 2. Number of researchers, publications and rate of publications per researcher by country
(2008–2017).

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Figura 3.

Share of mobile directional researchers by mobility type per country (2008–2017).

countries are the only MENA countries having a high-income level as per the World Bank
(2019UN). To a lesser degree, Morocco, Lebanon, Syria, and Jordan also have a higher share
of incoming scientists (~63%) than outgoing ones.

Several countries have larger shares of outgoing scholars (either as emigrants or outgoing
travelers) than incoming. Iran and Tunisia have the highest shares of outgoing scholars respec-
tively (71% E 66%). Iran and Syria show the highest rate of emigrant scientists. Turkey,
Egypt, Algeria, and Pakistan have similar shares, where around 52% of their mobile researchers
are emigrants or outgoing travelers. Qatar, Saudi Arabia, and United Arab Emirates are getting
the highest influx of researchers compared to very small outflows. D'altra parte, Syria,
Jordan, Iran, and Lebanon have the highest rate of outgoing flows. When comparing the shares
of emigrants and immigrants, Iran, Tunisia, and Syria are the only countries which show an
overall deficit of researchers.

3.2. Mobility Networks at the Regional and Country Levels

Prossimo, we look at the flows of scholars moving from and to MENA countries. Figura 4 offers an
overview of the mobility phenomenon for MENA scholars. All origins and destinations of sci-
entists affiliated to a MENA country at some point in time between 2008 E 2017 are grouped
by continent. It is worth noting that the MENA region is composed of countries located in
North Africa and West Asia.

Figura 4 shows the flows of mobile scientists at the regional level. Each node or vertical bar
represents a region. The size of the flow between two nodes represents the number of scientists
who have moved from a region to another. This figure shows that the MENA region has overall
more inbound than outbound flows. For all other regions, the inbound and outbound flows
have relatively the same size.

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Figura 4. MENA Mobility flows at the regional level (2008–2017).

The MENA region is highly connected with Europe based on the number of mobile scientists.
Europe is indeed the first mobility destination and origin, con 37% of the flows from/to MENA,
followed by North America (24%), MENA (20%), and Asia (16%). These findings suggest a rel-
atively high level of intra-MENA flows. Oceania, Africa, and South America show a much lower
circulation of scholars (less than 3%).

Prossimo, we analyze intercountry flows. Figura 5 shows the mobility flows of scholars moving
from and to the MENA region by countries labeled with their ISO Alpha-3 Codes. Only

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Figura 5. Mobility flows for scholars from/to MENA countries (2008–2017).

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Scientific mobility and collaboration in the Middle East and North Africa

countries with more than 350 mobile scientists between 2008 E 2017 are shown. IL
stati Uniti, France, United Kingdom, Germany, Canada, China, Malaysia, Italy, Japan,
and Australia are the main non-MENA destinations and origins. Inoltre, Figura 4 shows
that flows are not only limited to scholars moving from developing countries to developed
countries. When analyzing the origins and destinations of mobile scholars, the United States
appears to be the most common destination and origin for migrant scholars who were affil-
iated to an institution in the MENA region between 2008 E 2017.

When looking at specific MENA countries, some cases stand out. Per esempio, France is
the preferred destination for scholars originating from its former colonies in MENA, specifically
Morocco, Algeria, and Tunisia. North African countries also have strong ties with other coun-
tries in Europe, such as Spain, Germany, Svizzera, and the Netherlands. The United
Kingdom is one of the preferred destinations for GCC countries such as Saudi Arabia, IL
United Arab Emirates, and Qatar. Scholars from Egypt and Jordan have mostly migrated to
Saudi Arabia, ahead of the United States. Researchers from Pakistan migrate mainly from
and to China. Iraq and, to a lesser extent, Iran have major flows from and to Malaysia. In
the case of Iran, it is worth remembering that political sanctions by the United States have
had an impact on international scientific collaboration (Kokabisaghi, Miller et al., 2019).
Per esempio, Iranian scholars have been denied opportunities to attend international scientific
meetings during periods of sanctions. The blockade of the Iranian rial exchange has prevented
Iranian researchers from paying for publication, conference registration, and membership fees
in foreign currency.

We see within the top 15 destinations/origins of MENA migrant scholars that, except for
Pakistan and Iran, we can already find some countries outside of the region. Some of these
cases could be explained by geographical, cultural, historical, linguistico, and sociopolitical
proximities (Scott, 2015).

3.3.

Individual Characteristics of the Migrant Scientific Workforce: Gender and Academic Age

We now investigate the personal features of the migrant scholars by analyzing their distribu-
tion by academic age and gender. In terms of mobility, the migrant scholars represent the most
policy-relevant group, as they change their countries of affiliation, whereas the travelers keep
an affiliation with their suspected countries of origin. Figura 6 shows a population pyramid
based on the average age of emigrant and immigrant scholars in the MENA region.

The average academic age of migrant scholars in MENA between 2008 E 2017 era
12.39 years. For the whole MENA region, immigrants have an average academic age of
12.5 years versus 12.3 for the emigrants. For most countries, the immigrants are relatively
younger than emigrants, except for Iran, Palestine, Lebanon, and Turkey. The academic age
group “6–10” years is the most common for both emigrant and immigrant scholars. This group
represents around 42% of all the migrants. “11–15” is the second age group, representing 32%
of the migrant scientists. Migrant scholars with an academic age between 16 E 20 years
correspond to 10% of migrants. Other age groups represented less than 6%. Scholars who
migrated to GCC countries, Jordan, and Morocco were on average younger than emigrants
by 1.5 years from the same countries as represented in Appendix B in the Supplementary
Information. In this appendix, we focus only on emigrants and immigrants for countries where
more than 1,000 mobile researchers have been identified.

Male scholars represent 66% of all migrants in MENA and female authors account for 12%.
For the remaining 22%, gender was not reliably identified. These shares are similar when com-
paring between emigrants and immigrants. Tuttavia, we observe differences by country (Vedere

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Scientific mobility and collaboration in the Middle East and North Africa

Figura 6. Population pyramid of migrant scholars in MENA (2008–2017).

Appendix B in the Supplementary Information). Tunisia and Lebanon have the highest shares
of female emigrants, 22% E 21% rispettivamente. They are followed by Turkey, Algeria,
Morocco, and Iran with around 17% of female scholars. Pakistan and Egypt have a share
of around 11% of female migrant scientists. In the remaining countries, female authors rep-
resent shares below 10%, with the lowest shares (Di 7%) reached in Iraq, Saudi Arabia,
Syria, and Libya.

Figura 7 shows on the x-axis that almost all countries in MENA are dominated by male
researchers. The only countries for which the gender ratio is close to 1 are Tunisia,
Lebanon, and Turkey. The average male-to-female ratio for Iraq, Saudi Arabia, Jordan, UAE,
Qatar, and Pakistan exceeds 3. In the same figure, we also examine the gender ratios among
the migrant researchers for each country, and then compare them to the corresponding ratios
among all researchers.

Figura 7. Male-to-female gender ratios of migrants and all researchers by country (2008–2017).

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We notice a clear gender gap in terms of scientific mobility. In all countries, the gender dis-
parity is more severe among the migrant researchers. The male-to-female ratio among migrant
researchers is on average 2.5 times higher than the male-to-female ratio for all researchers.

3.4. Comparison of Collaboration and Mobility Networks

In the following, we compare the international scientific collaboration and mobility networks
of MENA countries. Figura 8 shows the MENA international collaboration network. Saudi
Arabia, Iran, Egypt, and Turkey drive most of the international cooperation within the region.
Tuttavia, the partnerships of these three countries seem to vary. While Saudi Arabia, Iran,
and Egypt show stronger collaboration links with some Asian countries, Turkey shows strong
collaboration linkages with several European countries, such as Germany and France. Nostro
findings are also consistent with the results previously published in the Towards 2030 report
(UNESCO, 2015): Iran has strong collaboration ties with developing countries. Malaysia is
among the top 10 collaborators, but Iran has a low share of papers with a foreign coauthor.
Ancora, we must note the role of the United States and the United Kingdom as important actors
within the network driving strong collaboration linkages with most of the MENA countries.

A few classical measures of network analysis are listed in Table 4 to describe the structures
of the collaboration and mobility networks. The numbers of nodes or countries linked to other
countries are different in the networks. Scholars in MENA migrate or travel to fewer countries
than they collaborate with. The number of edges represent the number of links between

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Figura 8. The main countries and links in the MENA collaboration network (2008–2017).
Coauthorship relations with at least one author from a MENA country and at least 100
copublications at the country level are included. For readability reasons, we show here the 100
strongest links between the countries. The colors of nodes represent world regions.

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Tavolo 4.

Structural measures of the MENA mobility and collaboration networks (2008–2017)

Structural measure
Number of vertices

Number of edges

Density

Average degree

Diameter

Clustering coefficient

Assortativity

Mobility
176

1,335

0.09

2.07

4.00

0.29

−0.72

Collaboration
215

3,124

0.14

1.87

3.00

0.24

−0.88

countries. The lower number of links in the mobility network is also reflected in the lower den-
sity. The density helps us to evaluate the degree of cohesion that exists between countries. IL
international coauthorships that we used to measure scientific collaborations tend to be more
frequent than international mobility. The mobility network has a thinner consistency than the
collaboration network in terms of affinity between countries (Wasserman & Faust, 1994).

MENA countries tend to collaborate at the international level to a higher degree than they
exchange human resources. Infatti, the international coauthorships that we used to measure
scientific collaborations tend to be more frequent than international mobility, which is a rarer
event. The assortativity coefficient is the Pearson correlation coefficient of degree between
pairs of linked nodes (Newman, 2002). Positive values indicate a correlation between nodes
of similar degree, while negative values indicate relationships between nodes of different
degree. The negative values of assortativity for both networks indicate that MENA countries
with small degrees tend to connect with countries with higher degrees.

Prossimo, we measured the degree and closeness for each MENA country in mobility and col-
laboration networks during the study period. Tavolo 5 lists centrality measures for each node to
describe the role of each country in collaboration and mobility.

The degree of a country represents the number of edges or countries it is connected to. IL
more a country has connections, the more influential it is in a network. All countries have a
lower degree in mobility compared to collaboration, as mentioned earlier.

Saudi Arabia has the highest degree in terms of mobility whereas Turkey has the highest
value in the collaboration network. Tuttavia, these two countries still top the MENA countries
rankings in terms of degrees. When comparing the ranks of the degree for each country, some
interesting values appear. Jordan and Morocco have the highest variation (−5) in terms of ranks
of degrees compared to other countries. These two countries have relatively much more in-
fluence in the collaboration network than in the mobility network. Iran also exhibits a similar
behavior. D'altra parte, Qatar has less influence in the collaboration than in mobility
when benchmarked to other MENA countries. Pakistan and Oman show similar variations in
terms of influence. Other countries have equivalent influences when degrees are ranked for
each network

When analyzing the closeness centrality, Turkey has also the highest closeness in collab-
oration and Saudi Arabia has the highest value in mobility. The variations of closeness ranks
are similar to the variations of degree ranks. The ranks of a given country in terms of degree

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Tavolo 5.

Centrality measures of MENA countries in collaboration and mobility (2008–2017)

Country
Algeria

Bahrain

Egypt

Iran

Iraq

Jordan

Kuwait

Lebanon

Libya

Morocco

Oman

Pakistan

Palestine

Qatar

Saudi Arabia

Syria

Tunisia

Turkey

UAE

Degree
64

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101

95

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59

68

73

45

85

86

104

29

89

121

54

87

120

94

Mobility

Collaboration

Closeness
0.61

Degree
169

Closeness
0.83

0.56

0.70

0.68

0.59

0.60

0.61

0.63

0.55

0.66

0.66

0.71

0.51

0.67

0.76

0.59

0.66

0.76

0.68

150

192

193

154

174

163

175

151

189

169

187

152

171

193

143

182

202

186

0.77

0.91

0.91

0.78

0.84

0.81

0.85

0.77

0.90

0.83

0.89

0.78

0.83

0.91

0.75

0.87

0.95

0.88

and closeness in each network are of the same levels of the rank of this specific country in
terms of number of scholars and publications. The MENA networks exhibit preferential con-
nectivity or preferential attachment to specific countries (Barabási & Albert, 1999) ad esempio
Turkey, Saudi Arabia, Iran, or Egypt. These countries or regional hubs play important roles
in network development. In addition to North American and European countries, leading
research countries in MENA tend to attract more researchers in terms of collaborative papers
and mobility flows.

Finalmente, for each country in MENA, we distinguish two types of relations in the mobility and
collaboration network: MENA–MENA relations and Non-MENA relations. Then, we compared
the shares of MENA–MENA with the Non-MENA relations for the mobility and the collabora-
tion phenomena for each individual country. Figura 9 shows the shares of collaboration and
mobility relations by type and by country in MENA between 2008 E 2017. Generalmente, both
collaborations and mobility exhibit a stronger international than regional focus from a MENA
perspective. From a country point of view, few cases such as Egypt or Saudi Arabia have a
higher share of mobility exchanges with other MENA than with Non-MENA countries. To a

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Figura 9. Percentage shares of MENA–MENA collaboration and mobility relations by country in MENA ordered by percentage of MENA–
MENA mobility ties (2008–2017).

lesser extent, Jordan and Kuwait also have a slightly higher share of MENA–MENA than Non-
MENA mobility exchanges. D'altra parte, Iran, Turkey, Morocco, Algeria, and Tunisia
have a relatively low share (12.5%) of their papers with an author from another MENA country.
These five countries show an average of 15% of mobility relations with the MENA region.

We also notice, for most countries in MENA, that the shares of MENA–MENA mobility rela-
tions are higher than the shares of MENA–MENA collaboration relations. From the MENA region
perspective, this suggests that the countries’ mobility links for these countries are more locally
focused than the collaborations.

4. DISCUSSION AND CONCLUSIONS

The main objective of this study was to better understand the scientific mobility flows in the
Middle East and North Africa region. We extended previous research on macrolevel indicators
studies of scientific mobility using bibliometric indicators. Several results of our study confirm
Scott’s “fluid globalization” framework (2015), where mobility is described as a “spectrum”
from the deeply rooted to the highly mobile scientists, with most scholars standing in the mid-
dle of that spectrum. Scientific mobility is a phenomenon within a wider context. The global-
ization of the economy, proximities (geographical, social, cultural, linguistico, E
sociopolitical), and the democratization of mobility, as well as internationalization, all influ-
ence scientific mobility. Some results also illustrate the “hegemonic internationalization”
framework (Scott, 2015). We observe large flows from/to Western Europe and the United
States. Some mobility linkages suggest also an “evolving core” including East Asian countries.
These two frameworks offer interesting aspects that we illustrate in our study. Tuttavia, Essi

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Scientific mobility and collaboration in the Middle East and North Africa

still focus on a single major “core” and “periphery” system. Leading research countries in
MENA also tend to attract researchers in terms of mobility flows. Infatti, the common cultural
spaces make international mobility easier for scholars. Although Scott (2015) qualifies this type
of scientific mobility as not “remarkable,” it is at least as important as mobility from the
“periphery” to the “core.” Scientific mobility is often perceived as a “brain-drain” with flows
from non-Western to Western countries. This also applies to MENA. “Brain-drain” is mainly
used to describe the flows from MENA to non-MENA countries, especially Western countries
(United States and Europe). This study allows us to understand mobility from a local per-
spective. Similar claims have been made in other fields: a single core–periphery system is
not efficient in cultural flows (Appadurai, 1996).

We now discuss in detail the main findings identified in our analysis. The country profiles,
as well as the demographic data of migrant scholars, are informative for policy-makers inter-
ested in the MENA region. In MENA, collaboration and mobility are quite aligned, although
mobility in MENA is larger as compared to other studies (Chinchilla-Rodríguez et al., 2018).
Some 12% of identified researchers have shown signs of international mobility. The mobile
scientists are mainly directional travelers, who represent 5.6% of the scholars in our data
set. Migrant is the second most common mobility type (3.2%). These shares illustrate the spec-
trum used by Scott (2015) to think about scientific mobility.

In this study, several characteristic patterns of the MENA region regarding the circulation of
scholars can be highlighted. The MENA region is highly connected with Europe based on the
number of mobile scientists. Europe is the first mobility destination and origin with 37% del
flows from/to MENA, followed by North America (24%), MENA (20%), and Asia (16%).
Oceania, Africa, and South America show a much lower circulation of scholars (less than
3%). In terms of international destinations, the MENA region has a relatively high level of
intraregional mobility flows.

(cid:129) Qatar, Saudi Arabia, United Arab Emirates, and Kuwait can be described as attracting

countries.

(cid:129) Turkey, Egypt, Pakistan, Morocco, Algeria, Jordan, and Lebanon are more balanced

countries.

(cid:129) Iran, Tunisia, Iraq, and Syria can be considered as sending countries.

The region is highly connected with Europe based on the mobility flows of scientists.
Europe is indeed the first mobility destination and origin, followed closely by North
America. Asia is the third preferred destination and origin. Oceania, Africa, and South
America show a much lower circulation of scholars from and to MENA. At the country level,
the United States, France, United Kingdom, Germany, Canada, China, Malaysia, Italy, Japan,
and Australia are the main non-MENA destinations and origins. We retrieve here most Western
countries mentioned by Scott (2015), with China and Malaysia from the Far East. Some cases
stand out when we look at specific MENA countries. Geographical, cultural, historical, linguis-
tic, and sociopolitical proximities have an influence on the mobility ties. Per esempio, questo è
the case for France, which is the preferred destination for scholars originating from its former
colonies in MENA, specifically Morocco, Algeria, and Tunisia. We also observe strong ties
between North African countries with other countries in Europe such as Spain, Germany,
Svizzera, and the Netherlands. The United Kingdom appears to be one of the preferred
destinations for scientists from GCC countries such as Saudi Arabia, the United Arab
Emirates, and Qatar. Scholars from Egypt and Jordan have mainly migrated to Saudi Arabia,
ahead of the United States. The observed flows confirm the geopolitical considerations

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mentioned by Scott (2015): attraction of ex-colonial powers or countries which speak “world”
languages, common cultural spaces, the key role of economic conditions, the “big country,
small country” effect, and political changes such as revolutions or civil unrest. Immigration
restrictions, sanctions, and travel bans affect mobility linkages, such as in the case of Iran
(Kokabisaghi et al. 2019). Except for Pakistan and Iran, we can already find some countries
outside of the region within the top 15 destinations/origins of MENA migrant scholars.
Researchers from Pakistan migrate mainly from and to China. Iraq and, to a lesser extent,
Iran have major flows from and to Malaysia. A previous study mentions that one in seven in-
ternational students in Malaysia was of Iranian origin in 2012 (UNESCO, 2015). Malaysia is
one of the rare countries that do not impose visas on Iranian citizens.

The sociopolitical environment, cooperation, and exchange programs could also contribute
to explain some of the observed mobility flows. Per esempio, the Pakistani Prime Minister
Nawaz Sharif referred to Pakistan and China as Iron Brothers when the two countries signed
In 2015 the China-Pakistan Economic Corridor (CPEC) (Vandewalle, 2015). The CPEC projects
play an important role in China’s One Belt One Road initiative. Later, In 2017, China and
Pakistan agreed to strengthen existing cooperation in science and technology. Europe and
Mediterranean countries have also signed several bilateral research and innovation coopera-
tion agreements, such as Tunisia (2004), Morocco (2005), Egypt (2008), Jordan (2010), E
Algeria (2013) (European Commission, 2019). As part of the 5+5 Dialogue, five countries from
the Arab Maghreb Union (Morocco, Algeria, Tunisia, Mauritania, and Libya) and five countries
from the Western Mediterranean (Spain, Malta, Portugal, Italy, and France) have regularly met
since 1990 to discuss a wide range of issues (security, economic cooperation, defense, migra-
zione, formazione scolastica, and renewable energy) (UNESCO, 2015). In September 2013, the meeting
focused on research and innovation and ministers of scientific research from these countries
signed the Rabat Declaration (2013). The ministers undertook the task of facilitating scientific
mobility by granting scientific researcher visas to promote the training of researchers and to
promote the transfer of technology and access to the scientific infrastructure.

From a demographic point of view, almost all MENA countries are dominated by male re-
searchers. Countries such as Saudi Arabia, Iran, Jordan, the United Arab Emirates, and Qatar
have shown high degrees of male dominance (Larivière, Ni et al., 2013). We notice that
Pakistan and Iraq also have a high gender ratio. Tunisia, Lebanon, and Turkey are the only
MENA countries for which the male-to-female ratio is close to 1. Compared to a developed
country like Germany, these findings are in contrast with some of the previously published
results by Zhao et al. (2021). In terms of mobility, mobile scholars in MENA are mainly
men with relatively senior academic status. These specificities are exacerbated in few coun-
tries, such as Saudi Arabia, Iraq, Syria, and Libya. Although GCC countries have a strong at-
traction for scholars, they seem to attract almost exclusively male researchers. There is a clear
gender gap in terms of scientific mobility. Men represent 66% of all migrants in MENA.
Women account for 12%. For the remaining authors, the gender was not identified reliably.
We notice similar shares when comparing emigrants and immigrants. Tuttavia, these shares
vary by country. Tunisia and Lebanon have the highest shares of female emigrants (22% E
21% rispettivamente). These two countries are followed by Turkey, Algeria, Morocco, and Iran
with around 17% of female migrant scholars. Egypt and Pakistan have a share of around 11%
of female migrant scholars. In the remaining countries, women account for less than 10% Di
migrant scientists, with the lowest shares in Iraq, Saudi Arabia, Syria, and Libya (Di 7%). In
all MENA countries, the gender disparity is more severe among the migrant researchers. IL
gender ratio among migrant scholars is on average 2.5 times higher than the gender ratio for all
researchers. Our analysis allows us to explore the extent of the gender gaps in the MENA

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region and to understand how these disparities vary between migrants and all researchers by
MENA country. The MENA countries have seen an increased participation of women in higher
formazione scolastica, particularly in the GCC countries, Dove 62% of enrolled students are female
(Jaramillo, Ruby et al., 2011). Although mobility is a means to opportunity (Hanson, 2010)
by providing access to people, networks, and infrastructures that make their research more
visible to influential researchers in their fields (Laudel, 2005), women are also more likely
to bear responsibilities for children and households (Ackers, 2008; Xie, Shauman, &
Shauman, 2003). Metcalfe (2008) has shown that there is much to be gained by policies in
the Middle East. Zippel (2011) has argued that national funding policies toward international
mobility of scientists have gendered implications. Policy-makers should adopt policies that
support family burdens on women which would help them in their careers and would result
in a more balanced research ecosystem (Karam & Afiouni, 2014). Such policies could include
policies and practices of more flexible and temporary mobility as suggested by Cañibano, Fox,
and Otamendi (2016).

The average academic age of migrant scholars was 12.39 years in MENA between 2008
E 2017. At the regional level, emigrants have an average of academic age of 12.3 years
versus 12.5 for immigrants. The academic age group “6–10” years is the most common for
the immigrant and emigrant scholars and represents around 42% of all the migrants. The second
age group is “11–15,” representing 32% of the migrant scientists. Migrant scholars with an ac-
ademic age between 16 E 20 years represent a share of 10% of all the migrant authors. Other
age groups had a share of less than 6%. As shown in Appendix B in the Supplementary
Information, the size of academic age group also varies by country. During the so-called
“Arab Spring,” young citizens clearly asked for more and better development opportunities.
The MENA countries stand at different levels of economic development, but they all share an
interest in the higher education supply and demand. From an internationalization perspective,
policies have implications for these three areas that have been discussed by a World Bank group
of authors for the MENA region (Jaramillo et al., 2011). The same authors have mentioned the
importance of looking at the policy framework to improve the quality and relevance of higher
education systems in the MENA region. Per esempio, they note that a key driver for internation-
alization is demographic trends. MENA countries have large young populations and increasing
numbers of students. To meet such high demand, cross-border higher education is widely used
by developing joint research and development programs. Traditional university partnerships,
probably the most common form of international mobility in higher education, also contribute
to mobility flows of PhD students, postdocs, and relatively more senior researchers.

Generalmente, both collaborations and mobility show a stronger international than regional
focus from the MENA region perspective. We note the role of the United States and United
Kingdom as important actors driving collaboration with most of MENA countries. Saudi
Arabia, Iran, Egypt, and Turkey drive most of the international cooperation within the region.
Tuttavia, their partnerships seem to vary. While Iran, Egypt, and Saudi Arabia have strong
collaboration ties with Asian countries, Turkey’s main collaborating countries include several
European countries, such as Germany and France.

From a country point of view, few cases, such as Egypt or Saudi Arabia, have a higher share
of mobility exchanges with other MENA than with Non-MENA countries. Allo stesso modo, but to a
lesser extent, Jordan and Kuwait have a slightly higher share of MENA–MENA than Non-
MENA mobility exchanges. D'altra parte, Iran, Turkey, Morocco, Algeria, and Tunisia
have a relatively low share (12.5%) of their papers with an author from another MENA coun-
try. For these five countries, the mobility relations with the MENA region represent 15% of all
their mobility linkages. On this aspect, there have been some calls at the First Arab-Euro

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Conference to develop stronger and closer collaboration between Arab countries to have more
Arab researchers returning to and more Europeans visiting the MENA region (Vesper, 2013).
For most countries in MENA, the shares of MENA–MENA mobility relations are higher than the
shares of MENA–MENA collaboration relations. From the MENA region perspective, this sug-
gests that the countries’ mobility links for these countries are more locally focused than the
collaborations.

In terms of methodology, this study represents a blueprint for how scientometric studies can
inform the mobility dynamics of specific countries and geographical regions. We acknowl-
edge that future studies are still necessary to further discuss the validity and reliability of scien-
tometric approaches to capture scientific mobility and its diversity. In Sugimoto et al. (2017)
there was already some discussion regarding the contextualization of scientometric mobility
data by comparing it to ORCID data, and this is an approach that will need more attention.
Tuttavia, the use of ORCID to validate scientific mobility, although relevant, is also not free of
its own limitations. Per esempio, ORCID (and arguably also other types of mobility survey
dati) suffers from limitations of coverage, representativeness, lack of standardization, E
completeness (Gomez, Herman, & Parigi, 2020). This means that currently there is no estab-
lished “golden set” to determine scientific mobility flows at the global level. Therefore, the use
of scientometric data to study scientific mobility must be seen as an informative but conser-
vative approach, needing to observe the intrinsic limitations coming from the method (cf.
Robinson-García et al., 2019), and whenever possible be used in combination with other
sources of mobility information. In line with this, in this paper, we intend to provide useful
material for the analysis and discussion of scientific mobility in the MENA region as well as
statistical information on issues raised already since the early 2000s by the Observatory of
International Migration in the Arab Region in collaboration with the United Nations (2002–
2018). We also complemented previous studies where data was limited to OECD countries as
destinations of scientists (Fargues, 2006; Özden, 2006). Future research should focus on ex-
panding these analytical capabilities to study other geographical areas (per esempio., South America,
Sub-Saharan Africa, Sahel region, OECD countries, and before and after Brexit effects). Such
analyses will be necessary to better support the assessment of different scientific systems, E
to determine how geopolitical decisions have an impact on the collaboration and circulation
of researchers and scientific ideas. The approach we used to measure mobility relies on track-
ing the change of author affiliation at the country level. We acknowledge that the taxonomy of
mobility used in our study is not absolute. Not every change of affiliation should be interpreted
as an indicator of breaking ties with the original country of the researcher, particularly in the
case of the travelers, who have multiple affiliations over time. Other classes could also be
introduced and discussed. There are many different types of mobility that could be derived,
such as return migrants or transients, as defined and used in other studies (Moed & Halevi,
2014; Subbotin & Aref, 2020). Future research may seek to use the approach presented by
Sugimoto et al. (2016) to estimate mobility at the regional, città, and institutional levels in
MENA, as well as including other typologies of mobility flows, such as the return of mobile
researchers, as well as the more transient type of mobility relationships (cioè., researchers with
just an occasional—one time—affiliation relationship with a country; cf. Moed and Halevi
(2014)). This granularity will enable us to capture the more domestic scholarly movements,
as well as the role of those researchers who in some way return to their countries of origin.
Such developments would substantially contribute to better inform the phenomenon of scien-
tific mobility by also incorporating more local and dynamic perspectives. We also plan to
combine the mobility indicators with other bibliometric information, such as citation metrics,
research areas, and funding acknowledgments. The further improvement and development of

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advanced scientometric mobility studies will also benefit decision-makers and science policy
analysts who look for programs and strategies that will encourage international collaborations
and mobility (per esempio., China Scholarship Council, Marie Sklodowska-Curie, or Ramón y Cajal
fellowships programs).

ACKNOWLEDGMENTS

We would like to thank Ludo Waltman and Thomas Franssen for providing valuable com-
ments on an earlier version of the manuscript. We are also grateful for the feedback from
two reviewers.

AUTHOR CONTRIBUTIONS

Jamal El-Ouahi: Conceptualization, Data curation, Formal analysis, Investigation,
Methodology, Software, Visualization, Writing—original draft. Nicolas Robinson-García:
Conceptualization, Formal analysis, Methodology, Writing—review & editing. Rodrigo
Costas: Conceptualization, Formal analysis, Methodology, Writing—review & editing.

COMPETING INTERESTS

Jamal El-Ouahi is an employee of Clarivate Analytics, the provider of the Web of Science.

FUNDING INFORMATION

Rodrigo Costas was partially funded by the South African DST-NRF Center of Excellence in
Scientometrics and Science, Tecnologia, and Innovation Policy (SciSTIP).

DATA AVAILABILITY

The research presented in this paper uses Web of Science data made available by Clarivate to
CWTS, Leiden University. We are not allowed to share this data. The statistics reported in this
study are available in a data repository (El-Ouahi, Robinson-García, & Costas, 2021).

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C
T
.

M

io
T
.

/

e
D
tu
q
S
S
/
UN
R
T
io
C
e
–
P
D

l

F
/

/

/

/

2
3
1
0
2
3
1
9
7
0
6
8
5
q
S
S
_
UN
_
0
0
1
4
9
P
D

/

.

F

B
sì
G
tu
e
S
T

T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

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