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An Evaluation of the Efficiency of Tertiary Education in the Explanation of the Performance of GDP per Capita Applying Data Envelopment Analysis (DEA)

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  • Marco Marto

    (Department of Social, Political and Territorial Sciences, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
    Research Unit in Governance, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

  • João Lourenço Marques

    (Department of Social, Political and Territorial Sciences, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
    Research Unit in Governance, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

  • Mara Madaleno

    (Research Unit in Governance, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
    Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

Abstract

The scientific literature and decision makers debate and explore education’s influence on regional development. However, differences among EU regions remain to be explained. The present article proposes to measure these disparites in terms of the gross domestic product (GDP) per capita and understand how tertiary education can explain that performance. A data envelopment analysis (DEA) optimization technique was applied along with spatial econometric models for all EU NUTS 2 regions (as defined by Eurostat for regional policies application), using the year 2020 as reference. The case of Portugal as a particular set of (seven) NUTS 2 regions included in the EU is detailed and analyzed in-depth. The two-stage least squares regression seems to explain well the differences in GDP per capita with the independent (and instrumental) variables which include the percentage of tertiary education and the spatial lags of this variable. The DEA optimization can support and help to explain most of the spatial regression results. The study identifies the NUTS 2 regions with the best favorable relationships among GDP per capita and percentages of tertiary education, predominantly located in the central and northern European countries and some in Ireland. The south EU regions, as expected, were identified as the regions with the poorest performances for GDP per capita and percentage of tertiary education, as well as some regions in eastern Europe. The positive and significant impact of the percentage of people with tertiary education on the values of GDP per capita given by the spatial econometric model suggests that special priority must be given to education and science in public policies agenda and government budget.

Suggested Citation

  • Marco Marto & João Lourenço Marques & Mara Madaleno, 2022. "An Evaluation of the Efficiency of Tertiary Education in the Explanation of the Performance of GDP per Capita Applying Data Envelopment Analysis (DEA)," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15524-:d:980509
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    References listed on IDEAS

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    1. Moritz Jellenz & Vito Bobek & Tatjana Horvat, 2020. "Impact of Education on Sustainable Economic Development in Emerging Markets—The Case of Namibia’s Tertiary Education System and its Economy," Sustainability, MDPI, vol. 12(21), pages 1-26, October.
    2. A. Z. Nowak & Gangadhar Dahal, 2016. "The contribution of education to economic growth: Evidence from Nepal," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 5(2), pages 22-41, June.
    3. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Gangadhar Dahal, 2016. "The contribution of education to economic growth: Evidence from Nepal," Proceedings of International Academic Conferences 3606124, International Institute of Social and Economic Sciences.
    6. Mabel Sanchez-Barrioluengo & Sara Flisi, 2017. "Student Mobility in Tertiary Education: institutional factors and regional attractiveness," JRC Research Reports JRC108895, Joint Research Centre.
    7. Butler, Timothy W. & Li, Ling, 2005. "The utility of returns to scale in DEA programming: An analysis of Michigan rural hospitals," European Journal of Operational Research, Elsevier, vol. 161(2), pages 469-477, March.
    8. Sterlacchini, Alessandro, 2008. "R&D, higher education and regional growth: Uneven linkages among European regions," Research Policy, Elsevier, vol. 37(6-7), pages 1096-1107, July.
    9. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    10. José M. Pastor & Carlos Peraita & Lorenzo Serrano & Ángel Soler, 2018. "Higher education institutions, economic growth and GDP per capita in European Union countries," European Planning Studies, Taylor & Francis Journals, vol. 26(8), pages 1616-1637, August.
    11. Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    12. Sarah Batterbury & Steve Hill, 2005. "Assessing the Impact of Higher Education on Regional Development: Using a Realist Approach for Policy Enhancement," Higher Education Management and Policy, OECD Publishing, vol. 16(3), pages 35-52.
    13. Cutrini, Eleonora, 2019. "Economic integration, structural change, and uneven development in the European Union," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 102-113.
    14. Monojit Chatterji, 1998. "Tertiary Education and Economic Growth," Regional Studies, Taylor & Francis Journals, vol. 32(4), pages 349-354.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, September.
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