Achy, Lahcen and Awad, Basil (2020): Gulf Cooperation Council Migration: A Longitudinal Migrant Network Approach.
Preview |
PDF
MPRA_paper_121478.pdf Download (1MB) | Preview |
Abstract
International migration is attracting growing interest in academia and policymakers in both hosting and sending countries. International migration is central in improving living standards of migrants and their families, supporting the sustainable development goals (SDGs) of the 2030 global development agenda, and has the potential to significantly boost world production. Although GCC migration is the third most important migration corridor in terms of total migrant stock, the topic has been underrepresented in the academic literature, which focuses mostly on South-North migration (Naufal 2015). The purpose of this paper is to fill this knowledge gap in two directions. First, the paper will be using an innovative dataset that estimates international migrant flow data instead of international migrant stock data. Migrant flow is more relevant as the changes in migrant stock are the outcome of demographic factors such as migrant births, migrant deaths, and migrant naturalization. Second, the paper will be applying network analysis to flow data. Recently, network analysis has been applied to other migration corridors, such as the US ( Charyyev et al., 2017 ) and Europe (Lenkewitz et. al 2019). Despite migration’s network character being established in migration studies, no research has so far been done on the GCC using network analysis. Two key preliminary findings emerge from this paper. First, it shows that the GCC corridor (made of six Arab Gulf countries) stands out as the most important migration corridor over the period 2005 to 2010 in terms of flow, and a close second over the period 2010 to 2015. When looking at just migrant stock data, the GCC’s significance is less pronounced. Second, after applying network analysis to migrant flow data, the paper reveals that the GCC is even more central to the global migration network when compared to its position with more standard econometric measures that ignore network effects. Our ranking measure is less destination biased than most migration literature (de Haas 2011). One potential explanation that the GCC (a net inflow destination leader) ranks even higher in a less destination-biased ranking measure is that over the past two decades, the GCC has emerged not only as a migration hub, but as a node strongly connected to other key international migration players. Future research can test network theories and identify regularities using (a) random graph methods, (b) strategic, game theoretic techniques, and (c) hybrid, statistical models. Such analysis can provide a network perspective to the more common analysis of push and pull factors between individual countries. Future research can also focus on individual GCC countries.
Item Type: | MPRA Paper |
---|---|
Original Title: | Gulf Cooperation Council Migration: A Longitudinal Migrant Network Approach |
English Title: | Gulf Cooperation Council Migration: A Longitudinal Migrant Network Approach |
Language: | English |
Keywords: | International Migration; Network Formation and Analysis Theory; Gulf Cooperation Council |
Subjects: | D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D85 - Network Formation and Analysis: Theory F - International Economics > F2 - International Factor Movements and International Business > F22 - International Migration |
Item ID: | 121478 |
Depositing User: | Lahcen Achy |
Date Deposited: | 29 Jul 2024 07:19 |
Last Modified: | 29 Jul 2024 07:19 |
References: | Abel, G.J., Cohen, J.E. (2019). Bilateral international migration flow estimates for 200 countries. Sci Data 6, 82. Abel, G., Sander, N. (2014). Quantifying Global International Migration Flows. Science, 343/6178, 1520-1522. Al-Ubaydli, O. (2015) The Economics of Migrant Workers in the GCC. The Arab Gulf States Institute in Washington, (Issue Paper #10). Ashtiani M., Jafari M., Mirzaie M., Razaghi-Moghadam, Z., Salehzadeh A., Wolkenhauer, O. (2018) A systematic survey of centrality measures for protein-protein interaction networks. University of Oxford Press. Ashtiani M., Jafari M., Mirzaie M. (2019). CINNA: An R/CRAN package to decipher Central Informative Nodes in Network Analysis. Azose, J. J. & Raftery, A. E. Estimation of emigration, return migration, and transit migration between all pairs of countries. Proc. Natl. Acad. Sci. 116, 116–122 (2019). Aleskerov, F., Mescheryakova, N., Rezypova, A., Shvydun, S. (2016). Network Analysis of International Migration. Mathematical Methods for Decision Making in Economics, Business and Politics Working Paper WP7/2016/06. High School of Economics National Research University in Moscow. Barrat A., Barthelemy M., Pastor-Satorras R., Vespignani A. (2004). The architecture of complex weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747. Bilecen, B., Gamper M., and Lubbers, M. (2018) The missing link: Social network analysis in migration and transnationalism. Social Networks, 53, (pp1-3). Bonacich, P. (1987). Power and Centrality: A Family of Measures. American Journal of Sociology, 92, 1170-1182. Charyyev, B., Goldade, T., and Gunes, Mehmet (2017) Network Analysis of Migration Patterns in the United States. Complex Networks, 689, (pp. 770-783). Clemens, M.. (2011). Economics and Emigration: Trillion-Dollar Bills on the Sidewalk?. Journal of Economic Perspectives. 25. 83-106. 10.2307/23049424. de Haas, H. (2011). The determinants of international migration. International Migration Institute, The University of Oxford. Erdos P, Renyi A. (1959). On Random Graphs. Publ. Math. Debrecen 6: 290 - 97 IMF (2013). Labor Market Reforms to Boost Employment and Productivity in the GCC. Annual Meeting of Ministers of Finance and Central Bank Governors. Jackson, M. (2011). An Overview of Social Networks and Economic Applications. Handbook of Social Economics. 1. 10.1016/B978-0-444-53187-2.00012-7. Jackson, M. (2014). Networks in the Understanding of Economic Behaviors. The Journal of Economic Perspectives. Jackson, M., Rogers, B., Zenou, Y. (2016). Networks: An Economic Perspective. Jackson, M., Rogers, B., Zenou, Y. (2017). The Economic Consequences of Social-Network Structure. Journal of Economic Literature. Lenkewitz S, Teney C., Windzio M. (2019). A network analysis of intra-EU migration flows: how regulatory policies, economic inequalities and the network-topology shape the intra-EU migration space, Journal of Ethnic and Migration Studies. Peres M., Xu H., Wu G. (2016). Community Evolution in International Migration Top1 Networks.PLoS ONE 11(2): e0148615. Porat, I., Benguigui, L.. (2016). Global migration topology analysis and modeling of bilateral flow network 2006–2010. EPL (Europhysics Letters). Naufal, G. (2015). The Economics of Migration in Gulf Cooperation Council Countries in Handbook of the Economics of International Migration (pp. 1597-1640). Oxford, UK: Elsevier. Robins, G. (2015) Doing Social Network Research. London, UK: Sage. World Bank (2016). Bilateral Remittances Matrix. World Bank (2018). The Job Agenda for the Gulf Cooperation Council Countries. Working Paper. World Bank (2019). World Development Indicators. Windzio, M. (2017). The network of global migration 1990–2013 Using ERGMs to test theories of migration between countries. Social Networks. UN DESA (2015). International Migration Flows to and from Selected Countries: the 2015 Revision. UN General Assembly (2015). Transforming our world : The 2030 Agenda for Sustainable Development, 21 October 2015. UN Population Division (2019). Total International Migrant Stock 2019. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121478 |