Beyond the Western Core-Periphery Model: Analysing Scientific Mobility and Collaboration in the Middle East and North Africa
BBeyond the Western Core-Periphery Paradigm: Analysing Scientific Mobility and Collaboration in the Middle East and North Africa
Jamal El Ouahi , Nicolas Robinson-Garcia , Rodrigo Costas [email protected] ; [email protected] Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, Netherlands Clarivate Analytics, Dubai Internet City, Dubai, United Arab Emirates [email protected] Delft Institute of Applied Mathematics (DIAM), TU Delft, Netherlands DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy, Stellenbosch University, Stellenbosch, South Africa
Abstract
This study investigates the scientific mobility and international collaboration networks in the Middle East and North Africa (MENA) region between 2008 and 2017. The main goal is to establish mobility and collaboration profiles at the region and country levels. By using affiliation metadata available in scientific publications, we track international scientific mobility and collaboration networks in the region. Three complementary approaches allow us to obtain a detailed characterization of scientific mobility. First, we study the mobility flows for each country to uncover the main destinations and origins of mobile scholars. Results reveal geographical, cultural, historical, and linguistic proximities. Cooperation and exchange programs also contribute to explain some of the observed flows. Second, we introduce mobile scientists’ academic age. The average academic age of migrant scholars in MENA between 2008 and 2017 was about 12.4 years. For most countries, immigrants are relatively younger than emigrants, except for Iran, Palestine, Lebanon, and Turkey. Scholars who migrated to Gulf Cooperation Council (GCC) countries, Jordan and Morocco were in average younger than emigrants by 1.5 year from the same countries. The academic age group 6-to-10 years is the most common for both emigrant and immigrant scholars. Third, we analyse gender differences of scholars. We observe a clear gender gap in terms of scientific mobility: Male scholars represent the largest group of migrants in MENA countries. We conclude discussing the policy relevance of the scientific mobility and collaboration aspects and discuss limitations and further research.
Keywords
International mobility, scientometrics indicators, internationalization, collaboration, research policy, research and development, Middle East and North Africa.
1. Introduction
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 innovation (Dokko & Rosenkopf, 2010; Mawdsley & Somaya, 2016; Palomeras & Melero, 2010; Singh & Agrawal, 2011; Slavova, Fosfuri, & De Castro, 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 cientific research (OECD, September 2008; Scellato, Franzoni, & Stephan, 2015). Similarly, international collaboration promotes the production of high-quality knowledge (Society, 2011) and is indispensable to solve complex scientific problems (Sonnenwald, 2007). Scholars would usually produce higher-impact research when moving and collaborating internationally (Franceschet & Costantini, 2010; Gazni, Sugimoto, & Didegah, 2012; Glanzel, 2001; Sugimoto et al., 2017; Van Raan, 1998). Several authors have addressed the scientific mobility from a sociological and an economical perspective (Baldwin, 1970; Beine, Docquier, & Rapoport, 2008; K. E. Boulding, 1966; Kenneth E 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. First, it 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 Africa and Latin America (SciDevNet, 2014-2019). Multiple innovative policy strategies even aimed at improving the ‘brain drain’ issue in regions such as in Asia (Krishna & Khadria, 1997; Song, 1997; Zweig, 1997). However, there is a clear uncertainty about the impact of international flows in academia. Other labels such as ‘brain gain’, ‘brain circulation’ 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 on ‘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 academic mobility are out-of-date. He uses two broad frameworks to describe and analyse 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 labelled as ‘fluid globalization’ focuses on the emergence of global communities, social movements and issues of development. Scott (2015) concludes that the ‘fluid globalization’ framework may be more useful to understand the trends in academic mobility. He describes the academic 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 understanding 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). On the other hand, collaboration and mobility are 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 capacities (Ackers, 2008), or to be considered as cientifically 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, institutions and individuals ‘to cope with globalization and to reap its benefits’ (Altbach & Knight, 2007). It is only until recently that bibliometric methods have offered a plausible solution to macro-level analyses of international mobility (Laudel, 2003; Sugimoto, Robinson-Garcia, & Costas, 2016). Computational advancements and especially the development of author name disambiguation algorithms, now allow tracking scientists mobility patterns based on changes in their affiliations 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 features such as multiple affiliation were not considered. To tackle this issue, Robinson-Garcia et al. (2019) proposed a taxonomy of mobility types based on the persistence in time of scientists’ linkage to countries. They distinguished between migrants and travellers . Migrants are characterized by having a cutting point in which they stop being affiliated to a country. Travelers maintain their linkage to a country, while adding other international affiliations (Ackers, 2005; Chinchilla-Rodríguez et al., 2018; Laudel, 2003; Robinson-Garcia et al., 2018; Robinson-Garcia et al., 2019; Sugimoto et al., 2016; Sugimoto et al., 2017). The use of a broader typology of mobility types opens the door to performing specific studies in different regions of the world and selected countries to better understand how they are integrated in the global network and how globalization affects specific geographical regions. Such broader mobility types nuance the dominant frameworks previously mentioned and commonly used by policy makers and research managers to understand scientific mobility in their countries or their institutions. 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 information (e.g., surveys), as well as allowing diachronic analyses. This paper contributes to Scott’s frameworks on ‘hegemonic internationalization’ and ‘fluid globalization’ where we focus on regional mobility linkages to analyse the scientific mobility phenomenon in the Middle East and North Africa region (MENA). MENA countries have made considerable investments in science and technology capacity to promote research and innovation (Schmoch, Fardoun, & Mashat, 2016; Shin, Lee, & Kim, 2012; Siddiqi, Stoppani, Anadon, & Narayanamurti, 2016). Such investments specifically target at the internationalisation of their domestic research. For this, attraction of foreign talent is a key element. Some outcomes of such investment are already visible, with some of these countries experiencing a recent growth of scientific production (Cavacini, 2016; Gul et al., 2015; Hassan Al Marzouqi, Alameddine, Sharif, & Alsheikh-Ali, 2019; Sarwar & Hassan, 2015). We also address the lack of reliable data. Several international experts’ groups have regularly met to discuss the international 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 perspective in all these papers which also highlight the poor quality or the lack of the migration data as well as the need of policies to enhance the benefits of migration for the development and the integration of the region. Özden (2006) presented the extent of the so-called 'brain drain' from MENA by using the dataset prepared by ocquier and Marfouk (2005). However, this data is limited to migration flows to OECD countries and ignores major destinations for scholars in MENA. In contrast to assuming MENA countries suffer from a brain drain in a more recent bibliometric study (Robinson-Garcia et al., 2019), we observed that countries such as Qatar, Iraq, Saudi Arabia or the United Arab Emirates were world leaders in terms of relative attraction of foreign scientists. Clearly, a more nuanced theoretical perspective is needed to understand mobility in the MENA region. In this paper, we focus on the MENA region aiming at better understanding what is going on 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: i.
To profile countries in the MENA region based on their mobile scientific workforce. ii.
To identify the main countries with which the MENA region interacts, distinguishing between origin and destinations of mobile scholars. iii.
To characterise 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. iv.
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 evidence about the most important mobility patterns in the region, thus providing better and more contextualized interpretations to the policies regarding the mobility of the scholarly workforce in the MENA countries. Moreover, the results deployed in this study can also work as evidence for policy makers from other countries and regions (e.g. Africa, EU, North America, Latin America, etc.) to understand the development of the MENA region regarding the internationalization of its workforce and its outcomes.
2. Data and Methods
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; Schulz, Mazloumian, Petersen, Penner, & Helbing, 2014; Torvik & Smalheiser, 2009). We used the approach proposed by Caron and van Eck (2014) which produces the most promising results as shown by Tekles and Bornmann (2019). 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 a total of 22.6 million disambiguated authors who have published around 18.2 million distinct papers irrespective of the document types. s per the World Bank (World Bank, October 2019), 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 commonly included in the MENA region (also often called Middle East, North Africa, Afghanistan, and Pakistan (MENAP ) and Middle East, North Africa and Turkey (MENAT )). Thus, we delimited our study to the 22 countries shown in Table 1. The dataset under study was comprised of 1,468,939 disambiguated authors who have contributed to 963,741 publications. Countries are also classified by using two groupings: The Scientific and Technological Capacity Index (STCI) (Wagner, Brahmakulam, Jackson, Wong, & Yoda, 2001) which classifies countries into four groups according to their scientific capacities: Advanced (Adv), Proficient (Pro), Developing (Dev), and Lagging (Lag). The Income level groups published by the World Bank (June 2019) which groups the countries into five groups based on their wealth intensity: High Income (H), Upper-middle income (UM), Lower-middle income (LM), and Low-income (L). Table 1. STCI (2001) and Income levels (2019) of MENA Countries
Country ISO Alpha-3 Code STCI (2001) Income Level (2019)
Afghanistan AFG Lag L Algeria DZA Lag UM Bahrain BHR Lag H Djibouti DJI Lag LM Egypt EGY Dev LM Iran IRN Dev UM Iraq IRQ Lag UM Jordan JOR Lag UM Kuwait KWT Dev H Lebanon LBN Lag UM Libya LBY Lag UM Morocco MAR Lag LM Oman OMN Lag H Pakistan PAK Dev LM Palestine PSE Lag LM Qatar QAT Lag H Saudi Arabia SAU Lag H Syria SYR Lag L Tunisia TUN Lag LM Turkey TUR Dev UM United Arab Emirates ARE Lag H Yemen YEM Lag L MENAT : https://en.wikipedia.org/wiki/MENA .2 Indicators
Table 2 lists the indicators we have used in our study as well as their definitions, how they are computed, and the types of data.
Table 2. Indicators types, definitions and calculations
Indicator Definition Calculation Type
Academic Origin Academic origin of an author. Researcher’s country affiliation on his first publication (Robinson-García, Cañibano, Woolley, & Costas, 2016; Sugimoto et al., 2017). Demography Academic Age Academic age of an author. Age of the researcher’s first publication (Nane, Larivière, & Costas, 2017). Gender Gender of an author (Male, Female or Not Available (N/A)) Gender is inferred by an algorithm based on three different APIs: Genderize.io, Gender-guesser & Gender API which consider the first name of the author and the suspected country of origin. Mobility type International mobility type of a researcher (see below). Taxonomy developed by Robinson-Garcia et al. (2019) based on changes of author’s affiliations. Mobility In this study, we use the taxonomy developed by Robinson-Garcia et al. (2019) which establishes the following mobility types: 1) not mobile , researchers who are always affiliated to the same country (e.g. country A); 2) migrants , those who leave at one point their country of first publication (e.g. they start in country A and are affiliated later with country B, and without further ties with country 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), 3) travellers (directional) , those who change countries but are linked to their country of origin throughout the study period (e.g., a researcher going from country A to A and B). We expand this typology to outgoing and incoming travellers (in the example before, A is the outgoing country, and B is the incoming country); and 4) travellers (non-directional) , researchers who are always linked to the same set of countries and hence we cannot establish the direction of movement (e.g. researchers affiliated to countries A and B in all the 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 travellers and incoming travellers. Regarding the travelers (non-directional), as noted by (Robinson-Garcia et al., 2019), more than half of the researchers assigned to this typology have published about 1 or 2 papers. This led the authors to consider that most of the potential errors derived from the disambiguation algorithm used in our study would probably be present in this group. To prevent from such limitation, in this tudy we exclude travelers (non-directional) from our analyses. Figure 1 shows the number of mobile researchers per country along with their mobility type. Figure 1. Number of mobile directional scholars per country and mobility type (2008 - 2017).
Considering the relatively low numbers of mobile authors in Djibouti, Bahrain, Afghanistan, Palestine and Yemen, these 5 countries were excluded from our dataset. To infer a gender to authors, we follow the same strategy as the one employed in the 2019 edition of the Leiden Ranking .We infer a gender based on researchers’ first name and their suspected country of origin. If no gender can be inferred, it is then considered unknown. The process is the following. First, for each author, one or more countries of origin are determined. 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 Gender API erforms better as evaluated in a previous study (Santamaría & Mihaljević, 2018). The first name of the author combined with a 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 inferred and have been labelled as N/A when the gender is unknown. We should keep in mind that these shares vary from country to country. We constructed co-authorship networks as a proxy to examine collaboration patterns within the scientific community in MENA. These networks are presented at the national level, with countries represented by nodes and the number of co-authored papers by vertices. Two countries are connected by an edge when at least one scholar from country A has co-authored a paper with a scientist from country B. In the case of the mobility networks, the methodology varies slightly. Here vertices 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 visualisations were created using VOSviewer (van Eck & Waltman, 2009).
It is important to acknowledge upfront that there are several limitations to the methods we used. First, our methods rely mainly on tracking the changes in authors affiliations to measure the mobility. Thus, researchers with low number of papers would most probably be underrepresented (Abramo, D’Angelo, & Solazzi, 2011). Second, certain types of mobility events, such as short-term stays, are not necessarily translated into publications. A third limitation is due to the coverage 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 the affiliation as an element of disambiguation. For example, an author with high frequency of affiliation change might be clustered into several different ‘authors’ by the algorithm. Also, 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 1 or 2 publications. These are considered as artefacts of the disambiguation of the algorithm and are excluded from our study. Also, the algorithm we used to infer the gender of authors is of course not perfect and we should keep these limitations in mind when analysing the results. Overall, the limitations discussed above point 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 recision in what is captured (i.e. 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. First, 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. Next, 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. Finally, we compare the mobility and the collaboration networks.
In Table 3, we summarize the number of disambiguated authors per country as well as the papers published during the study period. Authors affiliated to Iranian institutions show the highest productivity, followed by scholars in Turkey and Tunisia.
Table 3. Number of Scholars and Publications per country (2008-2017)
Country Researchers Papers Average number of Papers per Researcher
Algeria 58,753 25,359 0.43 Egypt 239,470 91,527 0.38 Iran 380,061 267,533 0.70 Iraq 22,769 8,219 0.36 Jordan 35,888 13,697 0.38 Kuwait 24,004 8,719 0.36 Lebanon 43,367 13,629 0.31 Libya 9,623 2,004 0.21 Morocco 121,256 20,476 0.17 Oman 24,838 6,656 0.27 Pakistan 235,220 73,449 0.31 Qatar 82,286 12,951 0.16 Saudi Arabia 222,576 95,264 0.43 Syria 9,484 3,134 0.33 Tunisia 73,904 39,086 0.53 Turkey 532,294 316,539 0.59 United Arab Emirates 55,828 19,239 0.34 Table 4 shows the number of researchers for each mobility type during the 2008-2017 period for the whole MENA region. Most researchers (82.9%) have not shown any sign of international mobility whereas around 12% have. Mobile scholars are mainly
Travellers (directional) , representing 5.6% of the researchers under study.
Migrant is the second most common type of mobility in MENA (3.2%), followed closely by
Traveller (non-directional) (3%).
Table 4. Researchers by mobility type in MENA (2008-2017) obility type Total Share Mobility Share Total Not Mobile
Mobile
Migrants
Traveller (directional)
Traveller (non-directional)
Insufficient information
It is worth noting 47,054 authors do not hold enough information either from the author disambiguation algorithm or from the mobility taxonomy which requires the publication of at least 2 publications to track the change of affiliations. These scientists were excluded from our analyses.
In this section we develop country profiles of the MENA region based on the mobile scientific workforce identified. In Figure 2, we show the share of mobile directional researchers by mobility type per country. Saudi Arabia, United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain, which form the Gulf Cooperation Council (GCC), all have higher share of incoming scholars (~77%) than outgoing. These six countries are the only MENA countries having a High-Income level as per the World Bank in 2019. To a lesser degree, Morocco, Lebanon, Syria and Jordan also have a higher share of incoming scientists (~60%) than outgoing ones. igure 2. Share of mobile directional researchers by mobility type per country (2008 - 2017).
Several countries have larger shares of outgoing scholars (either as emigrants or outgoing travellers) than incoming. Iran and Tunisia have the highest shares of outgoing scholars respectively 71% and 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 travellers. Qatar, Saudi Arabia and United Arab Emirates are getting the most influx of researchers compared to very small outflows. On the other hand, Syria, Jordan, Iran and Lebanon have the highest rate of outgoing flows. When comparing the percentage of outgoing scholars with the percentage of incoming scholars, Iran, Tunisia and Syria are the only countries which show an overall deficit of researchers.
Next, we look at the flows of scholars moving from and to MENA countries. Figure 3 offers an overview of the mobility phenomenon for MENA scholars. All origins and destinations of scientists affiliated to a MENA country at some point in time between 2008 and 2017 are grouped by continent. It is worth noting that the MENA region is composed by countries located in North Africa and West Asia.
The MENA region is highly connected with Europe based on the flow of mobile scientists, indeed, being the first mobility destination and origin, followed closely by North America and by Asia, respectively. Oceania, Africa, and South America show a much lower circulation of scholars.
Figure 3. MENA Mobility flows at the regional level (2008 - 2017).
Outgoing Incoming igure 3 shows that the MENA region has overall more inbound than outbound flows. The alluvial diagram also shows a high level of intra-MENA flows. For all MENA countries, the inbound and outbound flows have relatively the same size. Next, we analyse inter-countries flows. Figure 4 shows the mobility flows of scholars moving from and to the MENA region by countries and grouped by continent. Only countries with more than 350 mobile scientists between 2008 and 2017 are shown. United States, France, United Kingdom, Germany, Canada, China, Malaysia, Italy, Japan, and Australia are the main non-MENA destinations and origins. Furthermore, figure 4 shows that flows are not only limited to scholars moving from developing countries to developed countries. When analysing the origins and destinations of mobile scholars, United States appears to be the main destination and origin for migrant scholars who were affiliated to an institution in the MENA region between 2008 and 2017.
Figure 4. Mobility flows for scholars from/to MENA countries (2008 - 2017).
When looking at specific MENA countries, some cases stand out. For example, France is the preferred destination for scholars originating from its former colonies in MENA, specifically Morocco, Algeria and Tunisia. North African countries have also strong ties with other countries in Europe such as Spain, Germany, Switzerland and Netherlands. United Kingdom is one of the preferred destinations for GCC countries such as Saudi Arabia, the United Arab Emirates and atar. Scholars from Egypt and Jordan have mostly migrated to Saudi Arabia, ahead of 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 reminding that the political sanctions from the United States have had a clear impact on the scientific international collaboration linkages established (Kokabisaghi et al., 2019). 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, linguistic and socio-political proximities (Scott, 2015).
We now investigate the personal features of the migrant scholars by analysing their distribution 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 travellers keep an affiliation with their suspected countries of origin. Figure 5 shows a pyramid age 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 and 2017 was 12.39 years. For the whole MENA region, Immigrants have an average of 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 represent 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 and 20 years correspond to 10% of migrants. Other age groups represented less than 6%. Scholars who migrated to GCC countries, Jordan and Morocco were in average younger than emigrants by 1.5 year from the same countries as represented in Appendix A. In this appendix, we focus only on emigrants and immigrants for countries where more than 1,000 mobile researchers have been identified.
Figure 5. Pyramid age of migrant scholars in MENA (2008 - 2017).
We notice a clear gender gap in terms of scientific mobility. 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 comparing between emigrants and immigrants. However, we observe differences by country (see Appendix A.) Tunisia and Lebanon have the highest shares of female emigrants, 22% and 21% respectively. 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 represent shares below 10% with the lowest shares (about 7%) reached in Iraq, Saudi Arabia, Syria and Libya.
Following, we compare the international scientific collaboration and mobility networks of MENA countries. Figure 6 shows the MENA international collaboration network. Saudi Arabia, Iran, Egypt and Turkey seem to be central countries driving most of the international cooperation within the region. However, 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. Iran has also strong collaboration ties with developing countries. We retrieve the results previously published in the
Towards 2030 report: Malaysia is among the top 10 collaborators but Iran has a low share of papers with a foreign co-author. (Unesco, 2015).
Still, we must note the role of United States and United Kingdom as important actors within the network driving strong collaboration linkages with most of the MENA countries. igure 6. Main countries and links in the MENA Collaboration Network (2008-2017).
Co-authorship relations with at least one author from a MENA country and at least 100 co-publications at the country level are included. For readability reasons, we show here the 100 strongest links between the countries. Colours of nodes represent world regions
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 collaboration phenomena for each individual country. Figure 7 shows the shares of collaboration and mobility relations by type and by country in MENA between 2008 and 2017. In general, 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 lesser extent, Jordan and Kuwait also have a slightly higher share of MENA-MENA than Non-MENA mobility-exchanges. On the other hand, Iran, Turkey, Morocco, Algeria and Tunisia have a relatively low share (12.5%) of their papers with an author from another MENA country. These 5 countries show an average of 15% of mobility relations with the MENA region.
Figure 7. Shares of %MENA-MENA collaboration and mobility relations by country in MENA ordered by percentage of MENA-MENA mobility ties (2008-2017).
We also notice, 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 suggests that the countries mobility links for these countries are more locally focused than the collaborations. Discussion and conclusions
The main objective of this study was to better understand the mobility flows in the Middle East and North Africa region. We extended previous research on macro-level 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 middle of that spectrum. The scientific mobility is a phenomenon within a wider context. The globalisation of the economy, proximities (geographical, social, cultural, linguistic and socio-political), the democratisation of mobility as well as the internationalisation all influence the scientific mobility. Some results also illustrate the ‘Hegemonic internationalisation’ 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. hese two frameworks offer interesting aspects that we illustrate in our study. However, they still focus on a single ‘core’ and ‘periphery’ system. In the MENA region, the high level of intra-regional mobility flows suggests the existence of multiple ‘cores’ or local hubs. Indeed, the common cultural spaces make the 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 ‘brain-drain’ with flows from Non-West to West countries. This also applies to MENA. ‘Brain-drain’ is mainly used to describe the flows from MENA to non-MENA countries, especially Western countries (US and Europe). This study allows us to understand mobility from a less Western perspective. 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 interested 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). 12% of identified researchers have shown signs of international mobility. The mobile scientists are mainly
Directional Travellers who represent 5.6% of the scholars of our dataset.
Migrant is the second most common mobility type (3.2%) followed closely by
Non-Directional Travellers (3%). These shares illustrate the spectrum 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. In terms of international destinations, the MENA region has a high level of intra-regional mobility flows. As mentioned earlier, this particular pattern suggests the existence of regional ‘cores’. Qatar, Saudi Arabia, UAE, Kuwait can be described as attracting countries . We can also consider these countries as local ‘cores’ or ‘hubs’ in MENA. Turkey, Egypt, Pakistan, Morocco, Algeria, Jordan and Lebanon are more balanced countries . 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, 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, linguistic and socio-political proximities have an influence on the mobility ties. For example, this is 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, Switzerland and Netherlands. United Kingdom ppears 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 geo-political considerations 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, one in seven international students in Malaysia was of Iranian origin in 2012 (Unesco, 2015). Malaysia is one of the rare countries which do not impose visas on Iranian citizens. The socio-political environment, cooperation and exchange programs could also contribute to explain some of the observed flows in section 3.c. For example, 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 cooperation agreements such as Tunisia (2004), Morocco (2005), Egypt (2008), Jordan (2010) and Algeria (2013) (European Commission, March 2019). As part of the , 5 countries from the Arab Maghreb Union (Morocco, Algeria, Tunisia, Mauritania, Libya) and 5 countries from the Western Mediterranean (Spain, Malta, Portugal, Italy, France) have regularly met since 1990 to discuss a wide range of issues (security, economic co-operation, defence, migration, education 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 (Rabat Declaration, 2013).
The ministers undertook the task to facilitate the 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, mobile scholars in MENA are mainly men relatively academically senior. These specificities are exacerbated in few countries such as Saudi Arabia, Iraq, Syria and Libya.
Although GCC countries have a strong attraction of 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 the emigrants and immigrants. However, these shares vary by country. Tunisia and Lebanon have the highest shares of female emigrants, 22% and 21% respectively. 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% of migrant scientists with the lowest shares n Iraq, Saudi Arabia, Syria and Libya (about 7%). The average academic age of migrant scholars was 12.39 years in MENA between 2008 and 2017. At the regional level, Emigrants have an average of academic age of 12.3 years versus 12.5% for the Immigrants. The academic age group ‘6 – 10’ years is the most common for the immigrant and emigrant scholars and represent 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 and 20 years represent a share of 10% of all the migrant authors. Other age groups had a share of less than 6%. In general, both collaborations and mobility show a stronger international than regional focus from the MENA region perspective. We note the role of United States and United Kingdom as important actors driving collaboration with most of MENA countries. Saudi Arabia, Iran, Egypt and Turkey seem to be central countries in the collaboration network, driving most of the international cooperation within the region. However, 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. Similarly, but to a lesser extent, Jordan and Kuwait have a slightly higher share of MENA-MENA than Non-MENA mobility-exchanges. On the other hand, Iran, Turkey, Morocco, Algeria and Tunisia have a relatively low share (12.5%) of their papers with an author from another MENA country. For these 5 countries, the mobility relations with the MENA region represent 15% of all their mobility linkages. 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 suggests that the countries mobility links for these countries are more locally focused than the collaborations. In terms of methodology, this study represents a blueprint of how scientometric studies can inform the mobility dynamics of specific countries and geographical regions. This paper provides useful material for the analysis of scientific mobility in the MENA region as well as statistical information to 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 expanding these analytical capabilities in order to study other geographical areas (e.g. South America, Sub-Saharan Africa, Sahel region, OECD countries, before and after Brexit effects). Such analyses will be necessary to better support the assessment of different scientific systems, and to determine how geopolitical decisions have impact on the collaboration and circulation of researchers and scientific ideas. The approach we used to measure mobility relies on tracking the change of the author affiliation at the country level. Future research may seek to use the approach presented by Sugimoto et al. (2016) to estimate the mobility at the regional, city and institutional levels in MENA. This granularity will enable us to capture the more domestic scholarly movements and better inform the phenomenon of scientific mobility by also incorporating more local perspectives. We also plan to combine the mobility indicators with other bibliometric information such as citation metrics, research areas or funding cknowledgments. The further improvement and development of 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 (e.g. China Scholarship Council, Marie Sklodowska-Curie or Ramón y Cajal fellowships programs).
Author contributions
JEO: conceived the study, participated in its design and coordination, gathered data, generated figures, interpreted the data, and wrote the paper; NRG: participated in the design and coordination of the study, interpreted the data, and wrote the paper; RC: participated in the design and coordination of the study, interpreted the data, and wrote the paper.
Acknowledgments
We would like to thank Ludo Waltman and Thomas Franssen for providing valuable comments on an earlier version of the manuscript.
References
Abramo, G., D’Angelo, C. A., & Solazzi, M. (2011). The relationship between scientists’ research performance and the degree of internationalization of their research.
Scientometrics, 86 (3), 629-643. doi:10.1007/s11192-010-0284-7 Ackers, L. (2005). Moving people and knowledge: Scientific mobility in the European Union.
International Migration, 43 (5), 99-131. doi:10.1111/j.1468-2435.2005.00343.x Ackers, L. (2008). Internationalisation, Mobility and Metrics: A New Form of Indirect Discrimination?
Minerva, 46 (4), 411-435. doi:10.1007/s11024-008-9110-2 Altbach, P. G., & Knight, J. (2007). The Internationalization of Higher Education: Motivations and Realities.
Journal of Studies in International Education, 11 (3-4), 290-305. doi:10.1177/1028315307303542 Appadurai, A. (1996).
Modernity at large : cultural dimensions of globalization : Minneapolis, Minn: University of Minnesota Press. Backes, T. (2018, 2018).
Effective Unsupervised Author Disambiguation with Relative Frequencies . Baldwin, G. B. (1970). Brain drain or overflow?
Foreign Affairs, 48 (2), 358-372. Beine, M., Docquier, F., & Rapoport, H. (2008). Brain drain and human capital formation in developing countries: Winners and losers.
Economic Journal, 118 (528), 631-652. doi:10.1111/j.1468-0297.2008.02135.x Boulding, K. E. (1966). ECONOMICS OF KNOWLEDGE AND KNOWLEDGE OF ECONOMICS.
American Economic Review, 56 (2), 1-13. Boulding, K. E. (1966). The economics of knowledge and the knowledge of economics.
The American Economic Review, 56 (1/2), 1-13. Breschi, S., & Lissoni, F. (2009). Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows.
Journal of Economic Geography, 9 (4), 439-468. doi:10.1093/jeg/lbp008 añibano, C., & Woolley, R. (2015). Towards a Socio-Economics of the Brain Drain and Distributed Human Capital.
International Migration, 53 (1), 115-130. doi:10.1111/imig.12020 Caron, E., & van Eck, N. J. (2014).
Large scale author name disambiguation using rule-based scoring and clustering.
Paper presented at the Proceedings of the 19th international conference on science and technology indicators. Cavacini, A. (2016). Recent trends in Middle Eastern scientific production.
Scientometrics, 109 (1), 423-432. doi:10.1007/s11192-016-1932-3 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 . doi:10.3389/frma.2018.00017 Cota, R. G., Gonçalves, M. A., & Laender, A. H. (2007).
A Heuristic-based Hierarchical Clustering Method for Author Name Disambiguation in Digital Libraries.
Paper presented at the SBBD. Di Maria, C., & Stryszowski, P. (2009). Migration, human capital accumulation and economic development. (2), 306-313. doi:10.1016/j.jdeveco.2008.06.008 Docquier, F., & Marfouk, A. (2005). International Migration by Educational Attainment 1990–2000 (Release 1). Dokko, G., & Rosenkopf, L. (2010). Social Capital for Hire? Mobility of Technical Professionals and Firm Influence in Wireless Standards Committees. Organization Science, 21 (3), 677-695. doi:10.1287/orsc.1090.0470 Dokko, G., Wilk, S. L., & Rothbard, N. P. (2009). Unpacking prior experience: How career history affects job performance.
Organization Science, 20 (1), 51-68. doi:10.1287/orsc.1080.0357 European Commission. (March 2019). Mediterranean Partners. Retrieved from https://ec.europa.eu/research/iscp/index.cfm?pg=med_part Fargues, P. (2006).
International migration in the Arab region: Trends and policies.
Paper presented at the United Nations Expert Group Meeting on International Migration and Development in the Arab Region, Beirut. Franceschet, M., & Costantini, A. (2010). The effect of scholar collaboration on impact and quality of academic papers.
Journal of Informetrics, 4 (4), 540-553. doi:10.1016/j.joi.2010.06.003 Gazni, A., Sugimoto, C. R., & Didegah, F. (2012). Mapping World Scientific Collaboration: Authors, Institutions, and Countries.
Journal of the American Society for Information Science and Technology, 63 (2), 323-335. doi:10.1002/asi.21688 Glanzel, W. (2001). National characteristics in internationalscientific co-authorship relations.
Scientometrics, 51 (1), 69-115. doi:10.1023/a:1010512628145 Glytsos, N. P. (2010). Theoretical considerations and empirical evidence on brain drain grounding the review of Albania’s and Bulgaria’s experience1.
International Migration, 48 (3), 107-130. Gul, S., Nisa, N. T., Shah, T. A., Gupta, S., Jan, A., & Ahmad, S. (2015). Middle East: research productivity and performance across nations.
Scientometrics, 105 (2), 1157-1166. doi:10.1007/s11192-015-1722-3 Hassan Al Marzouqi, A. H., Alameddine, M., Sharif, A., & Alsheikh-Ali, A. A. (2019). Research productivity in the United Arab Emirates: A 20-year bibliometric analysis.
Heliyon, 5 (12), e02819. doi:10.1016/j.heliyon.2019.e02819 ayek, F. A. (1945). THE USE OF KNOWLEDGE IN SOCIETY.
American Economic Review, 35 (4), 519-530. International Labour Office. (2009).
International labour migration and employment in the Arab region: Origins, consequences and the way forward . Retrieved from Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.
The quarterly journal of economics, 108 (3), 577-598. doi:10.2307/2118401 Johnson, H. G. (1965). THE ECONOMICS OF THE BRAIN-DRAIN - THE CANADIAN CASE.
Minerva, 3 (3), 299-311. doi:10.1007/bf01099956 Kato, M., & Ando, A. (2017). National ties of international scientific collaboration and researcher mobility found in Nature and Science.
Scientometrics, 110 (2), 673-694. doi:10.1007/s11192-016-2183-z Kidd, C. V. (1965). THE ECONOMICS OF THE BRAIN-DRAIN.
Minerva, 4 (1), 105-107. doi:10.1007/bf01585988 Kokabisaghi, F., Miller, A. C., Bashar, F. R., Salesi, M., Zarchi, A. A. K., Keramatfar, A., . . . Vahedian-Azimi, A. (2019). Impact of United States political sanctions on international collaborations and research in Iran.
BMJ Global Health, 4 (5), e001692. doi:10.1136/bmjgh-2019-001692 Krishna, V., & Khadria, B. (1997). Phasing scientific migration in the context of brain gain and brain drain in India.
Science, Technology and Society, 2 (2), 347-385. Laudel, G. (2003). Studying the brain drain: Can bibliometric methods help?
Scientometrics, 57 (2), 215-237. doi:10.1023/a:1024137718393 League of Arab States. (2009).
Regional Report on Arab Labour Migration: Brain Drain or Brain Gain?
Retrieved from Cairo, Egypt: League of Arab States.: Levin, S. G., & Stephan, P. E. (1999). Are the foreign born a source of strength for US science? : American Association for the Advancement of Science. Lowell, B. L. (2003). The need for policies that meet the needs of all.
Science and Development Network . Mawdsley, J. K., & Somaya, D. (2016). Employee Mobility and Organizational Outcomes: An Integrative Conceptual Framework and Research Agenda. (1), 85-113. doi:10.1177/0149206315616459 Meyer, J. B. (2001). Network Approach versus Brain Drain: Lessons from the Diaspora. International Migration, 39 (5), 91-110. doi:10.1111/1468-2435.00173 Miguélez, E., & Moreno, R. (2013). Research Networks and Inventors' Mobility as Drivers of Innovation: Evidence from Europe.
Regional Studies, 47 (10), 1668-1685. doi:10.1080/00343404.2011.618803 Moed, H. F., Aisati, M. h., & Plume, A. (2012). Studying scientific migration in Scopus.
Scientometrics, 94 (3), 929-942. doi:10.1007/s11192-012-0783-9 Moed, H. F., & Halevi, G. (2014). A bibliometric approach to tracking international scientific migration. (3), 1987-2001. doi:10.1007/s11192-014-1307-6 Morano Foadi, S. (2006). KEY ISSUES AND CAUSES OF THE ITALIAN BRAIN DRAIN.
Innovation: The European Journal of Social Science Research, 19 (2), 209-223. doi:10.1080/13511610600804315 Mountford, A. (1997). Can a brain drain be good for growth in the source economy?
Journal of Development Economics, 53 (2), 287-303. doi:10.1016/s0304-3878(97)00021-7 ane, G. F., Larivière, V., & Costas, R. (2017). Predicting the age of researchers using bibliometric data.
Journal of Informetrics, 11 (3), 713-729. doi:10.1016/j.joi.2017.05.002 Nerad, M. (2010). Globalization and the internationalization of graduate education: A macro and micro view.
Canadian Journal of Higher Education, 40
Time magazine, 52,
United Nations Population, EGM/2006/10, New York, NY . Palomeras, N., & Melero, E. (2010). Markets for Inventors: Learning-by-Hiring as a Driver of Mobility.
Management Science, 56 (5), 881-895. doi:10.1287/mnsc.1090.1135 Rabat Declaration. (2013). Retrieved from https://pin.enssup.gov.ma/index.php/cooperation/cooperation-regionale/2-non-categorise/30-dialogue-5-5 Robinson-García, N., Cañibano, C., Woolley, R., & Costas, R. (2016).
Scientific mobility of early career researchers in Spain and The Netherlands through their publications.
Paper presented at the 21st International Conference on Science and Technology Indicators-STI 2016. Book of Proceedings. Robinson-Garcia, 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. arXiv preprint arXiv:1806.07815 . Robinson-Garcia, N., Sugimoto, C. R., Murray, D., Yegros-Yegros, A., Larivière, V., & Costas, R. (2019). The many faces of mobility: Using bibliometric data to measure the movement of scientists.
Journal of Informetrics, 13 (1), 50-63. doi:10.1016/j.joi.2018.11.002 Santamaría, L., & Mihaljević, H. (2018). Comparison and benchmark of name-to-gender inference services.
PeerJ Computer Science, 4 , e156. doi:10.7717/peerj-cs.156 Sarwar, R., & Hassan, S.-U. (2015). A bibliometric assessment of scientific productivity and international collaboration of the Islamic World in science and technology (S&T) areas.
Scientometrics, 105 (2), 1059-1077. doi:10.1007/s11192-015-1718-z Scellato, G., Franzoni, C., & Stephan, P. (2015). Migrant scientists and international networks.
Research Policy, 44 (1), 108-120. doi:10.1016/j.respol.2014.07.014 Schmoch, U., Fardoun, H. M., & Mashat, A. S. (2016). Establishing a World-Class University in Saudi Arabia: intended and unintended effects.
Scientometrics, 109 (2), 1191-1207. doi:10.1007/s11192-016-2089-9 Schulz, C., Mazloumian, A., Petersen, A. M., Penner, O., & Helbing, D. (2014). Exploiting citation networks for large-scale author name disambiguation.
EPJ Data Science, 3 (1). doi:10.1140/epjds/s13688-014-0011-3 SciDevNet. (2014-2019). Scott, P. (2015). Dynamics of Academic Mobility: Hegemonic Internationalisation or Fluid Globalisation.
European Review, 23 (S1), S55-S69. doi:10.1017/s1062798714000775 Shin, J. C., Lee, S. J., & Kim, Y. (2012). Knowledge-based innovation and collaboration: a triple-helix approach in Saudi Arabia.
Scientometrics, 90 (1), 311-326. doi:10.1007/s11192-011-0518-3 iddiqi, A., Stoppani, J., Anadon, L. D., & Narayanamurti, V. (2016). Scientific Wealth in Middle East and North Africa: Productivity, Indigeneity, and Specialty in 1981–2013.
PLoS One, 11 (11), e0164500. doi:10.1371/journal.pone.0164500 Singh, J. (2005). Collaborative Networks as Determinants of Knowledge Diffusion Patterns.
Management Science, 51 (5), 756-770. doi:10.1287/mnsc.1040.0349 Singh, J., & Agrawal, A. (2011). Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires.
Management Science, 57 (1), 129-150. doi:10.1287/mnsc.1100.1253 Slavova, K., Fosfuri, A., & De Castro, J. O. (2015). Learning by Hiring: The Effects of Scientists’ Inbound Mobility on Research Performance in Academia.
Organization Science . doi:10.1287/orsc.2015.1026 Society, R. (2011). Knowledge, networks and nations: Global scientific collaboration in the 21st century. Song, H. (1997). From brain drain to reverse brain drain: Three decades of Korean experience.
Science, Technology and Society, 2 (2), 317-345. Sonnenwald, D. H. (2007). Scientific collaboration.
Annual Review of Information Science and Technology, 41 (1), 643-681. doi:10.1002/aris.2007.1440410121 Stephan, P. E., & Levin, S. G. (2001).
Population Research and Policy Review, 20 (1/2), 59-79. doi:10.1023/a:1010682017950 Sugimoto, C. R., Robinson-Garcia, N., & Costas, R. (2016). Towards a global scientific brain: Indicators of researcher mobility using co-affiliation data.
OECD STI . Sugimoto, C. R., Robinson-Garcia, N., Murray, D. S., Yegros-Yegros, A., Costas, R., & Lariviere, V. (2017). Scientists have most impact when they're free to move.
Nature, 550 (7674), 29-31. doi:10.1038/550029a Tekles, A., & Bornmann, L. (2019). Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches. arXiv preprint arXiv:1904.12746 . Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE.
ACM Transactions on Knowledge Discovery from Data (TKDD), 3 (3), 1-29. Unesco. (2015).
UNESCO science report: towards 2030 . Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000235406 United Nations. (2002-2018).
Coordination Meeting on International Migration
Proceedings of Issi 2009 - 12th International Conference of the International Society for Scientometrics and Informetrics, Vol 2 (Vol. 2, pp. 886-897). Leuven: Int Soc Scientometrics & Informetrics-Issi. Van Raan, A. F. J. (1998). The influence of international collaboration on the impact of research results.
Scientometrics, 42 (3), 423-428. doi:10.1007/bf02458380 Vandewalle, L. (2015).
Pakistan and China: 'Iron brothers' forever?
Science and technology collaboration: Building capability in developing countries
Studies in Comparative International Development, 32 (1), 92-125. doi:10.1007/bf02696307
Appendix
In Appendix A, we represent the origins and the destinations of mobile researchers in alluvial diagrams. Here, the diagrams focus only on emigrants and immigrants for countries where we have more than 1,000 mobile researchers. We constructed the alluvial diagrams for each country as follows. They include three steps: -
The first is Gender, with three nodes,
Male , Female and
Not Available (N/A) . The size of the nodes is proportional to the number of nodes containing that value. -
The second step is Academic Age group. Also, in this case the size of each node is proportional to the number of scholars with the average academic age within each 5 years range. -
The third is Country (of origin for Immigrants or destination for Emigrants). The flows among nodes represent the number of scholars in our dataset sharing the combination of the three mentioned values: Gender-Academic Age-Country. We also limited our analysis to the top 15 origins and destinations by number of migrant scholars for each country.
The left charts represent the flows of scholar immigrants with their origins (
Immigrating from ). The right charts show the flows of scholar emigrants along with their destinations (
Emigrating to ). Appendix A. Migration flows of scholars per gender and age group (2008-2017).