gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2019-66gesj-1020Research ArticleSpecial issue: «Border and Coastal Areas of Greater Eurasia: Environmental and Socio-economic Challenges»European countries’ typology by the intensity of transboundary cooperation and its impact on the economic complexity levelRoosGöran

Adelaide 5042

VoloshenkoKsenia Y.

Kaliningrad

DrokTatiana E.

Kaliningrad

ZverevYury M.

Kaliningrad

yzverev@kantiana.ruAustralian Industrial Transformation Institute, Flinders UniversityAustraliaImmanuel Kant Baltic Federal UniversityRussian Federation202031032020131615Copyright © Roos G., Voloshenko K.Y., Drok T.E., Zverev Y.M., 20202020Roos G., Voloshenko K.Y., Drok T.E., Zverev Y.M.Roos G., Voloshenko K.Y., Drok T.E., Zverev Y.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/1020

Over recent years, it has become increasingly obvious that the countries, regions and individual systems are now developing within the framework of the emerging technological paradigm. The key elements for their development are knowledge and capabilities, being transformed into the products exported by a given country, these constitute the core of the economic complexity theory. In this article, the authors attempt to assess the long-term correlations between economic complexity and transboundary intensity drawing on the example of European countries. The authors developed a European Countries’ Typology according to their transboundary cooperation intensity. The paper establishes that the influence of the transboundary factor weakens as the economic complexity increases, and under certain conditions, it has a negative impact. It substantiates that the revealed relationships are due to the increasing role of global processes rather than transboundary ones as the economy becomes more complex and oriented towards the global market.

typologyEuropean countriestransboundary cooperationeconomic complexityheterogeneous panel analysisThe reported study was funded by RFBR and the government of the Kaliningrad region according to the research project № 19-410-390002 «Economic Complexity and Sector-Specific Strategy Selection by the regions of Russia in the context of the emerging value creation paradigm, using the case of the Kaliningrad region»ReferencesA more united and stronger central Europe needs transnational cooperation (2018). Input paper of the Interreg CENTRAL EUROPE Programme. [online]. Available at: www.interreg-central.eu/Content.Node/discover/Input-Paper.pdf [Accessed 11 May 2019].A more united and stronger central Europe needs transnational cooperation (2018). Input paper of the Interreg CENTRAL EUROPE Programme. [online]. Available at: www.interreg-central.eu/Content.Node/discover/Input-Paper.pdf [Accessed 11 May 2019].Alshamsi A., Pinheiro F.L. & Hidalgo C.A. (2018). Optimal diversification strategies in the networks of related products and of related research areas. Nature Communications, 9 (1). DOI: 10.1038/s41467-018-03740-9.Alshamsi A., Pinheiro F.L. & Hidalgo C.A. (2018). Optimal diversification strategies in the networks of related products and of related research areas. Nature Communications, 9 (1). DOI: 10.1038/s41467-018-03740-9.Aprausheva N.N. & Sorokin S.V. (2015). (Notes on gauss mixtures), VC RAN (in Russian.). DOI: 10.13140/RG.2.2.33609.34404.Aprausheva N.N. & Sorokin S.V. (2015). (Notes on gauss mixtures), VC RAN (in Russian.). DOI: 10.13140/RG.2.2.33609.34404.Baltagi B.H. (2005). Econometric Analysis of Panel Data. John Wiley & Sons. Boschma R. (2017). Relatedness as driver of regional diversification: A research agenda. Regional Studies, 51 (3), 351–364. DOI: 10.1080/00343404.2016.1254767.Baltagi B.H. (2005). Econometric Analysis of Panel Data. John Wiley & Sons. Boschma R. (2017). Relatedness as driver of regional diversification: A research agenda. Regional Studies, 51 (3), 351–364. DOI: 10.1080/00343404.2016.1254767.Brillet J.L. (2011). Structural Econometric Modelling: Methodology and Tools with Applications under EViews 2016. [online]. Available at: www.eviews.com/StructModel/structmodel.pdf [Accessed 14 Apr. 2019].Brillet J.L. (2011). Structural Econometric Modelling: Methodology and Tools with Applications under EViews 2016. [online]. Available at: www.eviews.com/StructModel/structmodel.pdf [Accessed 14 Apr. 2019].Compiani G. & Kitamura Y. (2016). Using mixtures in econometric models: a brief review and some new results. The Econometrics Journal, 19, 95–127. DOI: 10.1111/ectj.12068.Compiani G. & Kitamura Y. (2016). Using mixtures in econometric models: a brief review and some new results. The Econometrics Journal, 19, 95–127. DOI: 10.1111/ectj.12068.Díaz-Lanchas J., Llano C., Minondo A. & Requena F. (2018). Cities export specialization. Applied Economics Letters, Taylor & Francis Journals, 25 (1), 38–42. DOI: 10.1080/13504851.2017.1290784.Díaz-Lanchas J., Llano C., Minondo A. & Requena F. (2018). Cities export specialization. Applied Economics Letters, Taylor & Francis Journals, 25 (1), 38–42. DOI: 10.1080/13504851.2017.1290784.Dogan, E. & Aslan A. (2017). Exploring the relationship among CO2 emissions, real GDP, energy consumption and tourism in the EU and candidate countries: Evidence from panel models robust to heterogeneity and cross-sectional dependence. Renewable and Sustainable Energy Reviews, 77, 239–245. DOI: 10.1016/j.rser.2017.03.111.Dogan, E. & Aslan A. (2017). Exploring the relationship among CO2 emissions, real GDP, energy consumption and tourism in the EU and candidate countries: Evidence from panel models robust to heterogeneity and cross-sectional dependence. Renewable and Sustainable Energy Reviews, 77, 239–245. DOI: 10.1016/j.rser.2017.03.111.Druzhinin A.G., Gorochnya V.V., Gontar N.V., et al. (2017). Transboundary Clusters in the Coastal Cones of the European Part of Russia: Inventory, Typology, Factors, and Prospects, Baltic Region, 9 (4), 29–44. DOI: 10.5922/2074-9848-2017-4-2.Druzhinin A.G., Gorochnya V.V., Gontar N.V., et al. (2017). Transboundary Clusters in the Coastal Cones of the European Part of Russia: Inventory, Typology, Factors, and Prospects, Baltic Region, 9 (4), 29–44. DOI: 10.5922/2074-9848-2017-4-2.Felipe J. (2012). Tracking the Middle-Income Trap: What is It, Who is in It, and Why? Part 2, No 307, ADB Economics Working Paper Series, Asian Development Bank. Fortunato P. & Razo C. (2014). Export sophistication, growth and the middle-income trap. In Salazar-Xirinachs J. M., Nübler I. and KozulWright R. (eds.), Transforming Economies: Making Industrial Policy Work for Growth, Jobs and Development, International Labour Office, Geneva: ILO.Felipe J. (2012). Tracking the Middle-Income Trap: What is It, Who is in It, and Why? Part 2, No 307, ADB Economics Working Paper Series, Asian Development Bank. Fortunato P. & Razo C. (2014). Export sophistication, growth and the middle-income trap. In Salazar-Xirinachs J. M., Nübler I. and KozulWright R. (eds.), Transforming Economies: Making Industrial Policy Work for Growth, Jobs and Development, International Labour Office, Geneva: ILO.Foster N., Hunya G., Pindyuk O., Richter S. (2011). Revival of the Visegrad Countries’ Mutual Trade after their EU Accession: a Search for Explanation. Wiener Institut für Internationale Wirtschaftsvergleiche (wiiw). Research Reports 372. [online]. Available at: www.wiiw.ac.at/ revival-of-the-visegrad-countries-mutual-trade-after-their-eu-accession-a-search-for-explanation-dlp-2449.pdf [Accessed 11 May 2019].Foster N., Hunya G., Pindyuk O., Richter S. (2011). Revival of the Visegrad Countries’ Mutual Trade after their EU Accession: a Search for Explanation. Wiener Institut für Internationale Wirtschaftsvergleiche (wiiw). Research Reports 372. [online]. Available at: www.wiiw.ac.at/ revival-of-the-visegrad-countries-mutual-trade-after-their-eu-accession-a-search-for-explanation-dlp-2449.pdf [Accessed 11 May 2019].Green W.H. (2005). Econometric Analysis. Pearson Education International. Hartmann D., Bezerra M., Lodolo B. & Pinheiro F.L. (2019). International trade, development traps, and the core-periphery structure of income inequality. [online]. Available at: SSRN: www.ssrn.com/abstract=3312097 or DOI: 10.2139/ssrn.3312097.Green W.H. (2005). Econometric Analysis. Pearson Education International. Hartmann D., Bezerra M., Lodolo B. & Pinheiro F.L. (2019). International trade, development traps, and the core-periphery structure of income inequality. [online]. Available at: SSRN: www.ssrn.com/abstract=3312097 or DOI: 10.2139/ssrn.3312097.Hartmann D., Guevara M.R., Jara-Figueroa C., Aristara´N, M. & Hidalgo C.A. (2017). Linking Economic Complexity, Institutions, and Income Inequality. World Development, 93, 75–93. DOI: 10.1016/j.worlddev.2016.12.020.Hartmann D., Guevara M.R., Jara-Figueroa C., Aristara´N, M. & Hidalgo C.A. (2017). Linking Economic Complexity, Institutions, and Income Inequality. World Development, 93, 75–93. DOI: 10.1016/j.worlddev.2016.12.020.Hausmann R., Hidalgo C.A., Bustos S., Coscia M., Simoes A. & Yildirim M.A. (2011). The Atlas of Economic Complexity: Mapping Paths to Prosperity. MIT Press. Hidalgo C.A. (2018). From useless to keystone. Nature Physics, 14, 9–10. DOI: 10.1038/nphys4337.Hausmann R., Hidalgo C.A., Bustos S., Coscia M., Simoes A. & Yildirim M.A. (2011). The Atlas of Economic Complexity: Mapping Paths to Prosperity. MIT Press. Hidalgo C.A. (2018). From useless to keystone. Nature Physics, 14, 9–10. DOI: 10.1038/nphys4337.Hidalgo C.A. et al. (2018). The Principle of Relatedness. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. DOI: 10.1007/978-3-319-96661-8_46.Hidalgo C.A. et al. (2018). The Principle of Relatedness. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. DOI: 10.1007/978-3-319-96661-8_46.Hidalgo C.A. & Hartmann D. (2016). Economic complexity, institutions, and income inequality, OECD Insights. [online]. Available at: www. oecd-ilibrary.org/economics/debate-the-issues-complexity-and-policy-making_9789264271531-e [Accessed 31 Mar. 2019].Hidalgo C.A. & Hartmann D. (2016). Economic complexity, institutions, and income inequality, OECD Insights. [online]. Available at: www. oecd-ilibrary.org/economics/debate-the-issues-complexity-and-policy-making_9789264271531-e [Accessed 31 Mar. 2019].Hidalgo C.A. & Hausmann R. (2009). The building blocks of economic complexity. PNAS, 106 (26), 10570–10575. DOI: 10.1073/pnas.0900943106.Hidalgo C.A. & Hausmann R. (2009). The building blocks of economic complexity. PNAS, 106 (26), 10570–10575. DOI: 10.1073/pnas.0900943106.Hidalgo C.A., Klinger B., Barabási A.-L. & Hausmann R. (2007). The Product Space Conditions the Development of Nations. Science, 317 (5837), 482–487. DOI: 10.1126/science.1144581.Hidalgo C.A., Klinger B., Barabási A.-L. & Hausmann R. (2007). The Product Space Conditions the Development of Nations. Science, 317 (5837), 482–487. DOI: 10.1126/science.1144581.Hörnström L., Diş A.T., Berlina A. (2015). Added value of cross-border co-operation. Cross-border co-operation: still an undeveloped potential? Nordregio News, 1, 3–7. Javorcik B., Lo Turco A. & Maggioni D. (2017). New and improved: Does FDI boost production complexity in host countries? Economic Journal, 128 (614), 2507–2537. DOI: 10.1111/ecoj.12530.Hörnström L., Diş A.T., Berlina A. (2015). Added value of cross-border co-operation. Cross-border co-operation: still an undeveloped potential? Nordregio News, 1, 3–7. Javorcik B., Lo Turco A. & Maggioni D. (2017). New and improved: Does FDI boost production complexity in host countries? Economic Journal, 128 (614), 2507–2537. DOI: 10.1111/ecoj.12530.Korneevets V.S. (2010). (International regionalization in the Baltic Sea), Saint Petersburg (in Russian.). Kozhevnikova I., Shveikina V. & Domínguez E. (2012). Modelling Caspian Sea fluctuations. J. Flood Risk Manage, 5, 3–13. DOI: 10.1111/j.1753- 318X.2011.01116.x.Korneevets V.S. (2010). (International regionalization in the Baltic Sea), Saint Petersburg (in Russian.). Kozhevnikova I., Shveikina V. & Domínguez E. (2012). Modelling Caspian Sea fluctuations. J. Flood Risk Manage, 5, 3–13. DOI: 10.1111/j.1753- 318X.2011.01116.x.Land-based spatial planning and the added value of cross-border cooperation (2015). Background paper. Baltic SCOPE. 42 p. [online]. Available at: www.balticscope.eu/content/uploads/2016/11/Land-based-spatial-planning-and-the-added-value-of-cross-bordercooperation.Final_.Nov09.pdf [Accessed 11 May 2019].Land-based spatial planning and the added value of cross-border cooperation (2015). Background paper. Baltic SCOPE. 42 p. [online]. Available at: www.balticscope.eu/content/uploads/2016/11/Land-based-spatial-planning-and-the-added-value-of-cross-bordercooperation.Final_.Nov09.pdf [Accessed 11 May 2019].Mezhevich N.M., Zhuk N.P. (2013). Cross-border specialization of interregional interaction: applying new assessment methods. Baltic Region, 1 (15), 38-52. DOI: 10.5922/2074-9848-2013-1-3.Mezhevich N.M., Zhuk N.P. (2013). Cross-border specialization of interregional interaction: applying new assessment methods. Baltic Region, 1 (15), 38-52. DOI: 10.5922/2074-9848-2013-1-3.Neagu O. & Teodoru M.C. (2019). The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries. Sustainability, 11 (2), 497. DOI: 10.3390/su11020497.Neagu O. & Teodoru M.C. (2019). The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries. Sustainability, 11 (2), 497. DOI: 10.3390/su11020497.Neagu O. & Teodoru, M.C., (2019). The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries. Sustainability, MDPI, Open Access Journal, 11 (2), 1–29. DOI: 10.3390/su11020497.Neagu O. & Teodoru, M.C., (2019). The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries. Sustainability, MDPI, Open Access Journal, 11 (2), 1–29. DOI: 10.3390/su11020497.Pedroni P. (2000). Fully Modified OLS for heterogeneous cointegrated panels. In: Baltagi B.H., Nonstationary panels, panel cointegration and dynamic panels, Amsterdam: Elsevier, 93–130.Pedroni P. (2000). Fully Modified OLS for heterogeneous cointegrated panels. In: Baltagi B.H., Nonstationary panels, panel cointegration and dynamic panels, Amsterdam: Elsevier, 93–130.Pedroni P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series with an application to the PPP hypothesis. Economic Theory, 20 (3), 597–625. DOI: 10.1017/S0266466604203073.Pedroni P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series with an application to the PPP hypothesis. Economic Theory, 20 (3), 597–625. DOI: 10.1017/S0266466604203073.Perkmann, M. (2003). The rise of the Euroregion. A bird’s eye perspective on European cross-border co-operation. Lancaster University. Department of Sociology. 19 p. [online]. Available at: www.pdfs.semanticscholar.org/f420/d587e6e9801a95e450634970f33005ec1479.pdf [Accessed 11 May 2019].Perkmann, M. (2003). The rise of the Euroregion. A bird’s eye perspective on European cross-border co-operation. Lancaster University. Department of Sociology. 19 p. [online]. Available at: www.pdfs.semanticscholar.org/f420/d587e6e9801a95e450634970f33005ec1479.pdf [Accessed 11 May 2019].Practical Guide to Cross-border Cooperation. Third Edition (2000). Association of European Border Regions (AEBR). European Commission. [online]. Available at: www.aebr.eu/files/publications/lace_guide.en.pdf [Accessed 11 May 2019].Practical Guide to Cross-border Cooperation. Third Edition (2000). Association of European Border Regions (AEBR). European Commission. [online]. Available at: www.aebr.eu/files/publications/lace_guide.en.pdf [Accessed 11 May 2019].Radosevic S. (2017). Assessing EU Smart Specialization Policy in a Comparative Perspective. In Advances in the Theory and Practice of Smart Specialization, Academic Press, 1–36. DOI: 10.1016/B978-0-12-804137-6.00001-2.Radosevic S. (2017). Assessing EU Smart Specialization Policy in a Comparative Perspective. In Advances in the Theory and Practice of Smart Specialization, Academic Press, 1–36. DOI: 10.1016/B978-0-12-804137-6.00001-2.Ratnikova T.A. (2006). The introduction to the econometric analysis of panel data (in Russian), The Economic magazine of Higher school of economics, 3, 492–519.Ratnikova T.A. (2006). The introduction to the econometric analysis of panel data (in Russian), The Economic magazine of Higher school of economics, 3, 492–519.Reynolds, D. (2009). Gaussian Mixture Models. In: Li S.Z., Jain A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. Roos G. (2017). Technology-Driven Productivity Improvements and the Future of Work: Emerging Research and Opportunities. Hershey, PA: IGI Global, 1-255. DOI: 10.4018/978-1-5225-2179-2.Reynolds, D. (2009). Gaussian Mixture Models. In: Li S.Z., Jain A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. Roos G. (2017). Technology-Driven Productivity Improvements and the Future of Work: Emerging Research and Opportunities. Hershey, PA: IGI Global, 1-255. DOI: 10.4018/978-1-5225-2179-2.Roos G. & Kennedy N. (2014). Global Perspectives on Achieving Success in High and Low Cost Operating Environments. IGI Global, Hershey, PA, USA. DOI: 10.4018/978-1-4666-5828-8.Roos G. & Kennedy N. (2014). Global Perspectives on Achieving Success in High and Low Cost Operating Environments. IGI Global, Hershey, PA, USA. DOI: 10.4018/978-1-4666-5828-8.Roos G., Cheshire A., Nayar S., Clarke S. M. & Zhang W. (Eds.) (2019). Harnessing Marine Macroalgae for Industrial Purposes in an Australian Context: Emerging Research and Opportunities. Hershey, PA: IGI Global. Scott J.W. (2000). Euroregions, Governance and Transborder Co-operation within the EU. Borders, regions and people. European research in regional science, 10, 104–115.Roos G., Cheshire A., Nayar S., Clarke S. M. & Zhang W. (Eds.) (2019). Harnessing Marine Macroalgae for Industrial Purposes in an Australian Context: Emerging Research and Opportunities. Hershey, PA: IGI Global. Scott J.W. (2000). Euroregions, Governance and Transborder Co-operation within the EU. Borders, regions and people. European research in regional science, 10, 104–115.Scott J.W. (2017). Cross-Border, Transnational, and Interregional Cooperation. Richardson D. (Ed.) The International Encyclopedia of Geography. DOI: 10.1002/9781118786352.wbieg0616.Scott J.W. (2017). Cross-Border, Transnational, and Interregional Cooperation. Richardson D. (Ed.) The International Encyclopedia of Geography. DOI: 10.1002/9781118786352.wbieg0616.Seethalakshmi R., Saavithri V. & Vijayabanu C. (2014). Gaussian Scale Mixture Model for Estimating Volatility as a Function of Economic Factor, World Applied Sciences Journal, 32 (6), 1035–1038. DOI: 10.5829/idosi.wasj.2014.32.06.783.Seethalakshmi R., Saavithri V. & Vijayabanu C. (2014). Gaussian Scale Mixture Model for Estimating Volatility as a Function of Economic Factor, World Applied Sciences Journal, 32 (6), 1035–1038. DOI: 10.5829/idosi.wasj.2014.32.06.783.Socio-economic challenges, potentials and impacts of transnational cooperation in central Europe (2018). Final Report. The Vienna Institute for International Economic Studies (wiiw) and Interreg CENTRAL EUROPE. 266 p. [online]. Available at: www.interreg-central.eu/Content.Node/events/Final-Report-181018-full.pdf [Accessed 11 May 2019].Socio-economic challenges, potentials and impacts of transnational cooperation in central Europe (2018). Final Report. The Vienna Institute for International Economic Studies (wiiw) and Interreg CENTRAL EUROPE. 266 p. [online]. Available at: www.interreg-central.eu/Content.Node/events/Final-Report-181018-full.pdf [Accessed 11 May 2019].Stojkoski V. & Kocarev L. (2017). The Relationship Between Growth and Economic Complexity: Evidence from Southeastern and Central Europe. MPRA Paper, University Library of Munich, Germany. Sweet C. & Eterovic D. (2019). Do patent rights matter? 40 years of innovation, complexity and productivity. World Development, 115 (C), 78–93. DOI: 10.1016/j.worlddev.2018.10.009.Stojkoski V. & Kocarev L. (2017). The Relationship Between Growth and Economic Complexity: Evidence from Southeastern and Central Europe. MPRA Paper, University Library of Munich, Germany. Sweet C. & Eterovic D. (2019). Do patent rights matter? 40 years of innovation, complexity and productivity. World Development, 115 (C), 78–93. DOI: 10.1016/j.worlddev.2018.10.009.Utkovski Z., Pradier M.F., Stojkoski V., Perez-Cruz F. & Kocarev L. (2018). Economic complexity unfolded: Interpretable model for the productive structure of economies. PloS one, 13 (8), e0200822. DOI:10.1371/journal.pone.0200822.Utkovski Z., Pradier M.F., Stojkoski V., Perez-Cruz F. & Kocarev L. (2018). Economic complexity unfolded: Interpretable model for the productive structure of economies. PloS one, 13 (8), e0200822. DOI:10.1371/journal.pone.0200822.Verbeek M. (2003). A Guide to Modern Econometrics. John Wiley & Sons. Wassenberg B., Reitel B., Peyrony J., Rubió J. (2015). Territorial Cooperation in Europe - A Historical Perspective. Luxembourg: Publications Office of the European Union.Verbeek M. (2003). A Guide to Modern Econometrics. John Wiley & Sons. Wassenberg B., Reitel B., Peyrony J., Rubió J. (2015). Territorial Cooperation in Europe - A Historical Perspective. Luxembourg: Publications Office of the European Union.Wooldridge J.M. (2007). Econometric Analysis of Cross Section and Panel Data. MIT Press. 1.Wooldridge J.M. (2007). Econometric Analysis of Cross Section and Panel Data. MIT Press. 1.Zhu S. & Li R. (2017). Economic complexity, human capital and economic growth: empirical research based on cross-country panel data. Applied Economics, 49 (38), 3815–3828. DOI: 10.1080/00036846.2016.1270413.Zhu S. & Li R. (2017). Economic complexity, human capital and economic growth: empirical research based on cross-country panel data. Applied Economics, 49 (38), 3815–3828. DOI: 10.1080/00036846.2016.1270413.

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