Impact of Education and Economic Growth on Labour Migration in the European Union. A Panel Data Analysis

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dc.contributor.authorIstudor, Nicolae
dc.contributor.authorDinu, Vasile
dc.contributor.authorGogu, Emilia
dc.contributor.authorPrada, Elena-Maria
dc.contributor.authorPetrescu, Irina-Elena
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2020-11-25T08:54:54Z
dc.date.available2020-11-25T08:54:54Z
dc.description.abstractSince migration is considered to play an important role on the attainment of the sustainable development goals (SDG’s) this study analyses the reversed perspective of the migration-SDG’s nexus. The data set consists of 308 observations on 28 European Union countries (including the United Kingdom) over a time span of 11 years (between 2008 and 2018). The analysis employed various stages of estimation in order to compare different results obtained from the panel data regression models. Besides the classical panel data regression models, the paper includes the estimation of Arellano-Bover/Blundell-Bond model that uses the Generalized Method of Moments (also known as GMM) as an econometric tool to solve the endogeneity of the selected variables. The focus is on two sustainable development goals: labour and economic growth, and education of the European Union member states plus the United Kingdom. The results showed that there is a significant influence of the selected variables on the migration flows at the European Union level. Although there are some contradictory results regarding the direction and statistical significance of the link between the variables of interest, most estimators do not have fundamentally different results. The GDP per capita keeps its positive impact on migration by generating an immigration flow towards countries with high GDP per capita. Economic growth proves to be the main trigger of migration, while education also plays an important role in shaping migration. The importance of this study derives from the reversed perspectives analysis, considering migration as being directly influenced by the achievement of the sustainable development goals.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2020-4-004
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/158173
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonAhmad, M., & Khan, R. E. A. (2019). Does demographic transition with human capital dynamics matter for economic growth? A dynamic panel data approach to GMM. Social Indicators Research, 142(2), 753–772. https://doi.org/10.1007/s11205-018-1928-x
dc.relation.isbasedonBali Swain, R., & Yang-Wallentin, F. (2020). Achieving sustainable development goals: predicaments and strategies. International Journal of Sustainable Development & World Ecology, 27(2), 96–106, https://doi.org/10.1080/13504509.2019.1692316
dc.relation.isbasedonBaltagi, B. H., Kao, C., & Peng, B. (2016). Testing cross-sectional correlation in large panel data models with serial correlation. Econometrics, 4(4), 44. https://doi.org/10.3390/econometrics4040044
dc.relation.isbasedonBaum, C. F., & Christopher, F. (2006). An introduction to modern econometrics using Stata. College Station, TX: Stata press.
dc.relation.isbasedonBaum, C. F. (2014). Dynamic Panel Data estimators, EC 823: Applied Econometrics. Chestnut Hill, MA: Boston College. Retrieved from https://fmwww.bc.edu/EC-C/S2014/823/EC823.S2014.nn05.slides.pdf
dc.relation.isbasedonBoeri, T., Brücker, H., & Docquier, F. (2012). Brain drain and brain gain: The global competition to attract high-skilled migrants. Oxford: Oxford University Press.
dc.relation.isbasedonDeath, C. & Gabay, C., (2015). Doing biopolitics differently? Radical potential in the post-2015 MDG and SDG debates. Globalizations, 12(4), 597–612. https://doi.org/10.1080/14747731.2015.1033172
dc.relation.isbasedonde Haas, H. (2011). The determinants of international migration: Conceptualising policy, origin and destination effects (Working paper no. 2, DEMIG project paper 2). Siegen: DEMIG.
dc.relation.isbasedonEtzo, I. (2011). The determinants of the recent interregional migration flows in Italy: A panel data analysis. Journal of Regional Science, 51(5), 948–966. https://doi.org/10.1111/j.1467-9787.2011.00730.x
dc.relation.isbasedonFana, M., Torrejón Pérez, S., & Fernández-Macías, E. (2020). Employment impact of Covid-19 crisis: from short term effects to long terms prospects. Journal of Industrial and Business Economics, 47, 391–410. https://doi.org/10.1007/s40812-020-00168-5
dc.relation.isbasedonFaist, T. (2011). The Volume and Dynamics of International Migration and Transnational Social Spaces, Chapter 2: A Review of Dominant Theories of International Migration. Oxford: Oxford University Press, Scholarship Online.
dc.relation.isbasedonFehling, M., Nelson, B. D., & Venkatapuram, S. (2013). Limitations of the Millennium Development Goals: a literature review. Global Public Health, 8(10), 1109–1122. https://doi.org/10.1080/17441692.2013.845676
dc.relation.isbasedonFranc, S., Časni, A. Č., & Barišić, A. (2019). Determinants of migration following the EU enlargement: a panel data analysis. South East European Journal of Economics and Business, 14(2), 13–22. https://doi.org/10.2478/jeb-2019-0010
dc.relation.isbasedonGlobal Forum on Migration and Development. (2017). Migration Governance Framework (MiGOF). Retrieved August 1, 2020, from https://gfmd.org/pfp/ppd/5694
dc.relation.isbasedonHa, W., Yi, J., Yuan, Y., & Zhang, J. (2016). The dynamic effect of rural-to-urban migration on inequality in source villages: System GMM estimates from rural China. China Economic Review, 37, 27–39. https://doi.org/10.1016/j.chieco.2015.09.002
dc.relation.isbasedonHarris, R. D. F., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics, 91(2), 201–226. https://doi.org/10.1016/S0304-4076(98)00076-1
dc.relation.isbasedonHausman, J. A., & Pinkovskiy, M. (2017). Estimating dynamic panel models: backing out the Nickell Bias (Cemmap Working Paper CWP53/17). London: The Institute for Fiscal Studies, Department of Economics, UCL. https://doi.org/10.1920/wp.cem.2017.5317
dc.relation.isbasedonHolliday, J., Hennebry, J., & Gammage, S. (2019). Achieving the sustainable development goals: surfacing the role for a gender analytic of migration. Journal of Ethnic and Migration Studies, 45(14), 2551–2565. https://doi.org/10.1080/1369183X.2018.1456720
dc.relation.isbasedonHulme, D. (2009). The Millennium Development Goals (MDGs): A Short History of the World’s Biggest Promise (BWPI Working Paper No. 100). Manchester: The University of Manchester, GDI. https://doi.org/10.2139/ssrn.1544271
dc.relation.isbasedonInternational Labour Organization (ILO). (2015). ILO Global Estimates on Migrant Workers: Results and Methodology – Special Focus on Migrant Domestic Workers. Geneva: International Labour Office.
dc.relation.isbasedonInternational Organization for Migration. (2018). Migration and the 2030 Agenda: A Guide for Practitioners. Geneva: International Organization for Migration. Retrieved from https://publications.iom.int/books/migration-and-2030-agenda-guide-practitioners
dc.relation.isbasedonInternational Organization for Migration. (2019). Migration governance indicators. A global perspective. Geneva: International Organization for Migration. Retrieved from https://publications.iom.int/books/migration-governance-indicators-global-perspective
dc.relation.isbasedonKumar, S., Kumar, N., & Vivekadhish, S. (2016). Millennium Development Goals (MDGs) to Sustainable Development Goals (SDGs): Addressing Unfinished Agenda and Strengthening Sustainable Development and Partnership. Indian Journal of Community Medicine, 41(1), 1–4. https://doi.org/10.4103/0970-0218.170955
dc.relation.isbasedonLong, K., Vidal, E. S., Kuch, A., & Hagen-Zanker, J. (2017). Citizenship, migration and the 2030 Agenda for Sustainable Development (Briefing Series). London: ODI, Bern: Swiss Agency for Development and Cooperation (SDC). Retrieved from https://www.odi.org/sites/odi.org.uk/files/resource-documents/11744.pdf
dc.relation.isbasedonMallett, R. (2018). Decent work, migration and the 2030 agenda for sustainable development (Briefing Series). Bern: Swiss Agency for Development and Cooperation.
dc.relation.isbasedonMayda, A. M. (2010). International migration: A panel data analysis of the determinants of bilateral flows. Journal of Population Economics, 23(4), 1249–1274. https://doi.org/10.1007/s00148-009-0251-x
dc.relation.isbasedonMcGregor, E. (2020). Migration, the MDGs, and SDGs: Context and Complexity. In T. Bastia & R. Skeldon (Eds.), Routledge Handbook of Migration and Development. London: Routledge.
dc.relation.isbasedonMigration Data Portal. (2020). https://migrationdataportal.org/
dc.relation.isbasedonNapolitano, O., & Bonasia, M. (2010). Determinants of different internal migration trends: The Italian experience. The Manchester School, 80(4), 525–544. https://doi.org/10.1111/j.1467-9957.2012.02300.x
dc.relation.isbasedonNicolai, S., Wales, J., & Aiazzi, E. (2016). Education, migration and the 2030 Agenda for Sustainable Development (Briefing Series). London: ODI, Bern: Swiss Agency for Development and Cooperation (SDC). Retrieved from https://www.odi.org/sites/odi.org.uk/files/resource-documents/12422.pdf
dc.relation.isbasedonNyberg–Sørensen, N., Hear, N. V., & Engberg–Pedersen, P. (2002). The Migration–Development Nexus Evidence and Policy Options State–of–the–Art Overview. International Migration, 40(5), 3–47. https://doi.org/10.1111/1468-2435.00210
dc.relation.isbasedonO’Reilly, K. (2013). International migration and social theory. In The encyclopedia of global human migration. Wiley Online Library. https://doi.org/10.1002/9781444351071.wbeghm307
dc.relation.isbasedonOverseas Development Institute. (2017). Migration and the 2030 Agenda for Sustainable Development (Briefing Series). London: ODI, Bern: Swiss Agency for Development and Cooperation (SDC). Retrieved from https://www.odi.org/sites/odi.org.uk/files/resource-documents/12422.pdf
dc.relation.isbasedonParkins, N. C. (2011). Push and pull factors of migration. American Review of Political Economy, 8(2), 6–24.
dc.relation.isbasedonPiper, N. (2017). Migration and the SDGs. Global Social Policy, 17(2), 231–238. https://doi.org/10.1177/1468018117703443
dc.relation.isbasedonRoodman, D. (2009). How to do Xtabond2: An Introduction to Difference and System GMM in Stata. The Stata Journal, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106
dc.relation.isbasedonSitarz, D. (1993). Agenda 21: The Earth summit strategy to save our planet. Tucson, AZ: Earth Press. Retrieved from https://www.osti.gov/biblio/6289330
dc.relation.isbasedonSkeldon, R. (2008). Migration and Development. Paper presented on the United Nations Expert Group Meeting on International Migration and Development in Asia and the Pacific. September 20–21, 2008, Bangkok, Thailand. Retrieved from https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd_egm_200809_paper_skeldon_mig_and_dev.pdf
dc.relation.isbasedonSkeldon, R. (2014). Migration and Development: A global perspective. New York, NY: Routledge.
dc.relation.isbasedonSoylu, Ö. B., Çakmak, I., & Okur, F. (2018). Economic growth and unemployment issue: Panel data analysis in Eastern European Countries. Journal of International Studies, 11(1), 93–107. https://doi.org/10.14254/2071-8330.2018/11-1/7
dc.relation.isbasedonStataCorp. (2013). Stata glossary and index. Retrieved July 27, 2020, from https://www.stata.com/manuals13/xtxtunitroot.pdf
dc.relation.isbasedonStataCorp. (2015). Stata treatment-effects reference manual. College Station, TX: Stata Press Publication.
dc.relation.isbasedonTeixeira, A. A. C., & Queirós, A. S. S. (2016). Economic growth, human capital and structural change: A dynamic panel data analysis. Research policy, 45(8), 1636–1648. https://doi.org/10.1016/j.respol.2016.04.006
dc.relation.isbasedonTorres-Reyna, O. (2007). Panel data analysis fixed and random effects using Stata (v. 4.2) (Presentation 112). Princeton, NJ: Priceton University, Data & Statistical Services.
dc.relation.isbasedonUllah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
dc.relation.isbasedonUnited Nations. (1993). Report of the United Nations Conference on Environment and Development, Rio de Janeiro, June 3–14, 1992. New York, NY: United Nations.
dc.relation.isbasedonUnited Nations. (2005). Millennium Project. Investing in development: A practical plan to achieve the Millennium Development Goals. New York, NY: United Nations.
dc.relation.isbasedonUnited Nations General Assembly. (2017). Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. Retrieved from https://www.howandwhentoreference.com/
dc.relation.isbasedonUsher, E. (2005). The millennium development goals and migration. Geneva: International Organization for Migration.
dc.relation.isbasedonWickramasinghe, A. A. I. N., & Wimalaratana, W. (2016). International migration and migration theories. Social Affairs, 1(5), 13–32. https://doaj.org/article/90df97f1572447a7b64dccff72c43429
dc.relation.isbasedonWooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectlabour migrationen
dc.subjectsustainable development goalsen
dc.subjectquality educationen
dc.subjectdecent work and economic growthen
dc.subjectpanel dataen
dc.subjectdynamic GMMen
dc.subject.classificationC23
dc.subject.classificationF22
dc.subject.classificationO15
dc.subject.classificationQ01
dc.titleImpact of Education and Economic Growth on Labour Migration in the European Union. A Panel Data Analysisen
dc.typeArticleen
local.accessopen
local.citation.epage67
local.citation.spage55
local.facultyFaculty of Economics
local.filenameEM_4_2020_4
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue4
local.relation.volume23
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