Abstract
High-skilled migration is increasingly important in our global society. Although there is now a quite wide literature on the phenomenon, much remain to be learned. This contribution briefly explores the past, present and possible future in the study of high-skilled migration. The availability of more, and better, individual data in recent years is paramount in advancing our knowledge on causes, effects and patterns of high-skilled migration. However, there is first a need to clearly identify what is meant by “high-skilled” migration to make the body of literature more consistent and, second, the new data should be used to do research grounded on solid theory. All in all, the opportunities are many and the future in the field looks bright.
Migration is an expression of the human aspiration for dignity, safety and a better future. It is part of the social fabric, part of our very make-up as a human family.
—Ki-Moon (2013)
Access provided by Autonomous University of Puebla. Download chapter PDF
Similar content being viewed by others
Keywords
1 Introduction
Migrating is intrinsic in human nature. Campbell and Barone (2012), in their study on the origin of human migration, point out that proofs of human migration found in fossils date back at least 2 million years. The basic principle of migrating is clear, people move to “improve” their situation, but the meaning of “improve” has been changing over time and it is different across space. Initially individuals moved for survival reasons. As societies evolved, this sort of “forced” migration was gradually substituted with other forms of migration, where the scope was not strict survival, but rather ensuring a better quality of life and living conditions. Although forced migration still exists in situations of conflict or extreme poverty, the majority of migration movements nowadays has a more “voluntary” component. Migrants move to get more opportunities, often linked to the labour market. High-skilled migrants are becoming an increasingly important part of migration flows. As Nathan (2014) highlights, in the decade 2000–2010, high-skilled (international) migrants (identified as individuals with a tertiary degree) in the OECD countries increased by 70%, as compared to 20% for the low-skilled. As Faggian et al. (2017) point out labour markets have become increasingly globalized, which has created new opportunity for mobility, especially of high-skilled workers. Acostamadiedo et al. (2020) expect a further increase in the number of migrants to the European Union by 2030. They propose four different scenarios and find an increase in 2030 of between 21% and 44% from the recorded average annual figure for the 2008–2017 period. The figure is much larger—between 83% and 208%—when restricted only to high-skilled labour migration. As such, migration is now a more “selective” process that has attracted a lot of attention of both academics and policy-makers. However, although the extensive, and growing, literature on the topic, much confusion still exists and more needs to be studied and understood. This short contribution aims at highlighting what we know on the topic, while also paving the way for possible future developments. Section “How Do We Define “High-Skilled” Migration?” starts by discussing what is meant by “high-skilled” migration; the other three brief sections sketch the main issues dealt with in the past, the improvements in the present and some possible future advancements.
2 How Do We Define “High-Skilled” Migration?
It is often the case that high-skilled migration is discussed without making explicit what is meant by “high-skilled.” In reality, the way high-skilled migration is defined and measured in the literature is not univocal and there is no agreed concept, partly because the migrants themselves do not constitute a homogenous group (Salt, 1997). The three ways to identify high-skilled migrants found in the literature are based on: a. educational attainment; b. occupation; c. salary or income. Although the three alternatives are somewhat positively correlated, a contribution by Parsons et al. (2020) shows that their correlation is not as strong as previously thought. For instance, by looking at recent international immigrants to the USA, using data from the American Community Survey for the year 2015, they find that only 8% of migrants can be defined as high-skilled if all three conditions, that is, a tertiary education, an occupation in the upper tier of the US Standard Occupational Classification and a salary of at least $100,000, are to be combined. If salary is relied upon in isolation, potentially 87% of otherwise classified high-skilled migrants may be omitted, if occupation is used this number goes down to 53%, but it is still very high. The most comprehensive measure is educational attainment (only 9% excluded). This reason, together with data availability, provides a strong justification in favour of education as a means to identify high-skilled migrants. While recognizing that also education has obvious limitations, education is therefore now the most common way to define high-skilled migrants (e.g. Pekkala et al., 2016; Parey et al., 2017; Basile et al., 2020; Crown et al., 2020a), although there are also examples of occupation-based definitions (e.g. Czaika & Parsons, 2017).
3 Past: What Do We Know About “High-Skilled” Migration?
Given that the initial study of migration goes back as far as Ravenstein in 1885, there is a lot we know about migration (including migration of highly skilled individuals), in particular with regards to two aspects:
-
(a)
Migration determinants
-
(b)
Migration consequences
Migration determinants include both individual and regional characteristics. Among the individual characteristics, some established facts include that age is negatively correlated with the probability of migrating, while education and skills are positively associated with it (Faggian et al., 2015). The reasons for the latter are many. Education helps in finding and processing information more efficiently (DaVanzo, 1983), it also implies a more “globalized” search for jobs, as educated individuals are more selective in picking their employment (Schwartz, 1976). More educated individuals also rely less on family and friends (DaVanzo, 1981) and are generally more adaptable to new places, in that they are more “receptive to change” (Levy & Wadycki, 1974) and extrovert (Crown et al., 2020b). In OECD countries, between 2000 and 2015, there has been a continuous increase in the level of education of immigrants, and a simultaneous decline in the proportion of poorly educated ones.
As for regional characteristics, it is clear that individuals move to regions that offer not only better economic and labour market opportunities (Greenwood, 1985), but also better amenities (Graves, 1976, 1980). The balance between the two depends on the characteristics of the migrants themselves. For instance, younger migrants place more importance on labour market variables, while retirement migration is often linked to quality-of-life factors. The income of migrants affects their choices too, with richer migrants being able to give amenities a higher value.
As for the consequences of high-skilled migration, they are often supposed to be positive for the destinations, for example, in terms of increased productivity and innovation (Faggian & McCann, 2009), and negative for the origins, in the form of reduced growth and brain-drain (Beine et al., 2008; Wong & Yip, 1999). However, although maybe a bit counterintuitive, some argue that also origins might benefit from the emigration of highly skilled individuals, for instance, in the form of remittances, future return migration (also called brain-circulation) and the creation of networks between destinations and origins. Kanbur and Rapoport (2005) go an extra mile and point out that ex-ante emigration prospects could also foster the formation of human capital in the origin pushing more individuals to invest in their education. If not all of them emigrate, then those left behind represent an added-value for the origins.
4 Present: Improving Our Current Knowledge
In the past two decades, more and better data on migrants, both interregional and international, became available. This helped refining some of the previous work on both the determinants and consequences of migration. For instance, although a positive link between high-skilled migration and innovation was found in different contributions, all of them relied on patents to measure innovation, but, in time, it became clear that patents were a poor way to measure innovation especially for the sectors where high-skilled migrants were actually working.
As Faggian et al. (2017) state, most highly-skilled migrants end up working in advanced service sectors where patents heavily underestimate innovation. Pinate et al. (2021) extend the analysis of innovation, in the case of Italy, by using not only patents, but also other intellectual property rights (IPRs), that is, trademarks and design rights. As for the determinants, better individual data means that it is possible to focus more on gender issues. The majority of migration studies in the past focused primarily on the experiences of male migrants (as head of the households) or did not distinguish between genders. This is a common flaw in the labour-related literature, but with better data it is possible to address the role of female migrants in more detail. This is of paramount importance because contributions on female migration are still rather scarce (e.g. Faggian et al., 2007) and results are mixed.
5 Future: Big Leaps with Big Data?
With the advent of so-called big data, there is a huge opportunity to further progress our knowledge of migration. Ascani et al. (2021), for instance, using data from Facebook, were able to study mobility patterns within and across local labour markets in Italy and the effect of the recent pandemic. The potential of these data is clear as they are high-frequency (three observations per day) and provide very large samples (about 2.5 million observations). However, with big potential come also possible big problems. First of all, it is easy to get lost in such a large amount of data and being tempted into data-driven, and not theory-driven, research. Second, there is the need to find appropriate tools of analysis (models) and even appropriate hardware and software. Third, there are issues of privacy and anonymity, which might prevent from getting detailed information on the characteristics of the migrants, such as their educational level or even age and gender, which are often the variables of interest.
References
Acostamadiedo, E., Sohst, R., Tjaden, J., Groenewold, G., & de Valk, H. (2020). Assessing Immigration Scenarios for the European Union in 2030 – Relevant, Realistic and Reliable? International Organization for Migration, Geneva, and the Netherlands Interdisciplinary Demographic Institute, the Hague.
Ascani, A., Faggian, A., Montresor, S., & Palma, A. (2021). Mobility in Times of Pandemics: Evidence on the Spread of COVID19 in Italy’s Labour Market Areas. Structural Change and Economic Dynamics, 58, 444–454.
Basile, R., De Benedictis, L., Durban, M., Faggian, A., & Mínguez, R. (2020). The Impact of Immigration on the Internal Mobility of Natives and Foreign-Born Residents: Evidence from Italy. Spatial Economic Analysis. https://doi.org/10.1080/17421772.2020.1729997
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.
Campbell, B. C., & Barone, L. (2012). Evolutionary Basis of Human Migration. In B. C. Crawford & M. H. Campbell (Eds.), Causes and Consequences of Human Migration (pp. 45–64). Cambridge: Cambridge University Press.
Crown, D., Faggian, A., & Corcoran, J. (2020a). High Skilled Immigration and the Occupational Choices of Native Workers: The Case of Australia. Oxford Economic Papers, 72(3), 585–605.
Crown, D., Gheasi, M., & Faggian, A. (2020b). Interregional Mobility and the Personality Traits of Migrants. Papers in Regional Science, in press. https://doi.org/10.1111/pirs.12516
Czaika, M., & Parsons, C. (2017). The Gravity of High-Skilled Migration Policies. Demography, 54, 603–630.
DaVanzo, J. (1983). Repeat Migration in the United States: Who Moves Back and Who Moves On? Review of Economics and Statistics, 65, 552–559.
DaVanzo, J. (1981). Microeconomic Approaches to Studying Migration Decisions. In R. W. Gardner (Ed.), Migration Decision Making: Multidisciplinary Approaches to Microlevel Studies in Developed and Developing Countries. Pergamon Press.
Faggian, A., Corcoran, J., & Partridge, M. (2015). Interregional Migration Analysis. In C. Karlsson, M. Andersson, & T. Norman (Eds.), Handbook in the Research of Methods and Applications in Economic Geography (pp. 468–490). Edward Elgar.
Faggian, A., & McCann, P. (2009). Human Capital, Graduate Migration and Innovation in British Regions. Cambridge Journal of Economics, 33(2), 317–333. https://doi.org/10.1093/cje/ben042
Faggian, A., McCann, P., & Sheppard, S. (2007). Some Evidence That Women Are More Mobile Than Men: Gender Differences in UK Graduate Migration Behavior. Journal of Regional Science, 47(3), 517–539.
Faggian, A., Rajbhandari, I., & Dotzel, K. (2017). The Interregional Migration of Human Capital and Its Regional Consequences: A Review. Regional Studies, 51(1), 128–143.
Graves, P. E. (1976). A Reexamination of Migration, Economic Opportunity and the Quality of Life. Journal of Regional Science, 16(1), 107–112.
Graves, P. E. (1980). Migration and Climate. Journal of Regional Science, 20, 227–237.
Greenwood, M. J. (1985). Human Migration: Theory, Models and Empirical Studies. Journal of Regional Science, 25(4), 521–544.
Kanbur, R., & Rapoport, H. (2005). Migration Selectivity and the Evolution of Spatial Inequality. Journal of Economic Geography, 5(1), 43–57.
Ki-Moon B. (2013). Secretary-General's remarks to High-Level Dialogue on International Migration and Development. Retrieved September 1, 2021, from https://www.un.org/sg/en/content/sg/statement/2013-10-03/secretary-generals-remarks-high-level-dialogue-international
Levy, M., & Wadycki, W. (1974). Education and the Decision to Migrate: An Econometric Analysis of Migration in Venezuela. Econometrica, 42(2), 377–388.
Nathan, M. (2014). The Wider Economic Impacts of High-Skilled Migrants: A Survey of the Literature for Receiving Countries. IZA Journal of Migration, 3(4).
Parey, M., Ruhose, J., Waldinger, F., & Netz, N. (2017). The Selection of High-Skilled Emigrants. The Review of Economics and Statistics, 99(5), 776–792.
Parsons, C. R., Rojon, S., Rose, L., & Samanani, L. (2020, September). High Skilled Migration through the Lens of Policy Migration Studies, 8(3), 279–306. https://doi.org/10.1093/migration/mny037
Pekkala Serr, S., Kerr, W., Çaglar, Ö., & Parsons, C. (2016). Global Talent Flows. Journal of Economic Perspectives, 20(4), 83–106.
Pinate, A., Faggian, A., Di Berardino, C., & Castaldi, C. (2021). Migration and Innovation: Measuring the Effect by Origin and Skill Level on Patents, Trademarks, and Designs Across Italian Provinces. GSSI Discussion Paper.
Ravenstein, E. G. (1885). The Laws of Migration. Journal of the Statistical Society, 48, 167–227.
Salt, J. (1997). International Movements of the Highly Skilled. OECD Report.
Schwartz, A. (1976). Migration, Age and Education. Journal of Political Economy, 24, 701–720.
Wong, K., & Yip, C. K. (1999). Education, Economic Growth, and Brain Drain. Journal of Economic Dynamics and Control, 23(5–6), 699–726.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Faggian, A. (2022). High-Skilled Migration: Past, Present and Future. In: Goulart, P., Ramos, R., Ferrittu, G. (eds) Global Labour in Distress, Volume I. Palgrave Readers in Economics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89258-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-89258-6_5
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-89257-9
Online ISBN: 978-3-030-89258-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)