- + South + East + er (2) g(GDP)r represents the 2009-2019 average growth rate of GDP as a function of the initial (2009) level of GDP (GDPrt−1), the average growth rate of high-skilled labour, g(HCr) as a measure of human capital, the average investment growth rate g(Ir), and the composite innovation index (RDr). The model also includes regional dummies South and East to capture structural differences in Eastern and Southern Europe, respectively. Similar to the spatial panel approach, we include cluster dummies (Industry_clustersr). Parameter α represents the intercept term.
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- Data The panel dataset covers various macroeconomic regional and sectoral indicators between 2009 to 2019 sourced from Eurostat. This panel include gross domestic product, gross value added, gross fixed capital formation, and labour productivity (all expressed in constant prices). The indicators are either measuring the total economy of a region or sectors of it (agriculture, mining, manufacturing, and energy supply, public services, construction, as well as knowledge-intensive and less knowledge-intensive services). The dataset also includes demographic indicators such as regional population density, active population values as well as the level of employees’ education (tertiary, secondary and primary), R&D expenditures and the size of the regions (in km), among other factors.
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Elburz, Z., Nijkamp, P., Pels, E., 2017. Public infrastructure and regional growth: Lessons from meta-analysis. Journal of Transport Geography 58, 1–8. https://doi.org/10.1016/j.jtrangeo.2016.10.013 The Future of EU Cohesion | Page 27 European Commission, 2022a. Cohesion in Europe towards 2050: Eighth report on economic, social and territorial cohesion. Publications Office of the European Union, Luxembourg. https://doi.org/10.2776/624081 European Commission, 2022b. Establishing a European Declaration on Digital rights and principles for the Digital Decade (Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions), COM(2022) 27 final.
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- European Parliament Research Service, Kiss, M., 2017. Digital skills in the EU labour market. https://doi.org/10.2861/451320 Eurostat, 2022. Employment by A*10 industry breakdowns [WWW Document]. URL https://ec.europa.eu/eurostat /web/products-datasets/product?code=nama_10_a10_e Fujita, M., Thisse, J.-F., 1996. Economics of Agglomeration. Journal of the Japanese and International Economies 10, 339–378. https://doi.org/10.1006/jjie.1996.0021 Hulten, C.R., Schwab, R.M., 1991. Public Capital Formation and the Growth of Regional Manufacturing Industries. National Tax Journal 44, 121–134.
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https://doi.org/10.1016/S1574-0684(05)01006-3 Acemoglu, D., Johnson, S., Robinson, J.A., 2001. The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review 91, 1369–1401. https://doi.org/10.1257/aer.91.5.1369 Alexandri, E., Fragkiadakis, K., Fragkos, P., Lewney, R., Paroussos, L., Pollitt, H., 2018. A technical analysis on decarbonisation scenarios - constraints, economic implications and policies. Technical Study on the Macroeconomics of Energy and Climate Policies.
- IMPRESSUM Herausgeber, Verleger, Eigentümer und Hersteller: Verein „Wiener Institut für Internationale Wirtschaftsvergleiche“ (wiiw), Wien 6, Rahlgasse 3 ZVR-Zahl: 329995655 Postanschrift: A 1060 Wien, Rahlgasse 3, Tel: [+431] 533 66 10, Telefax: [+431] 533 66 10 50 Internet Homepage: www.wiiw.ac.at Nachdruck nur auszugsweise und mit genauer Quellenangabe gestattet.
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- Offenlegung nach 25 Mediengesetz: Medieninhaber (Verleger): Verein "Wiener Institut für Internationale Wirtschaftsvergleiche", A 1060 Wien, Rahlgasse 3. Vereinszweck: Analyse der wirtschaftlichen Entwicklung der zentral- und osteuropäischen Länder sowie anderer Transformationswirtschaften sowohl mittels empirischer als auch theoretischer Studien und ihre Veröffentlichung; Erbringung von Beratungsleistungen für Regierungs- und Verwaltungsstellen, Firmen und Institutionen. wiiw.ac.at https://wiiw.ac.at/p-6560.html
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- Page 48 | The Future of EU Cohesion A8. Data for key factors Table A11: Overview data used for key factors Variable Unit Description Source GDP Euro (purchasing power standards), 2019 Gross domestic product ARDECO database GDP growth Per cent Annual average growth rates ARDECO database High-skilled employment Per cent of total employment Share of employed with completed tertiary education in total employment Eurostat Institutional quality Index value Quality of governance indicator, developed by the Quality of Government Institute, Gothenburg University Social Progress Index 2020 Infrastructure Index value Composite index including information on the regions’ accessibility by road, rail and air.
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Rodríguez-Pose, A., Garcilazo, E., 2015. Quality of Government and the Returns of Investment: Examining the Impact of Cohesion Expenditure in European Regions. Regional Studies 49, 1274–1290. https://doi.org/10.1080/00343404.2015.1007933 Rodrik, D., Subramanian, A., Trebbi, F., 2004. Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development. Journal of Economic Growth 9, 131–165.
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- The Future of EU Cohesion | Page 37 The estimated results are presented in Table A3. The negative and significant effect of the initial level of GDP indicates that regions with a lower initial GDP level grow at a faster rate. Investment growth boosts economic growth, as indicated by the positive and significant coefficient of the investment variable. Likewise, human capital contributes to regional growth implying the importance of knowledge and skills for the growth potential. Innovation, as measured by the innovation composite index, has a positive effect on regional growth, confirming the well-established finding that innovation contributes to economic growth and competitiveness (Polder et al., 2009; Vujanović et al., 2022).
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- The second econometric approach (spatial cross-section) focusses on cross-sectional data observed at a single point in time. Hence, this approach lacks the time dimension but allows for the inclusion of a greater number of factors that can influence growth. This allows us to investigate whether less developed regions grow faster than more developed regions. Hence, we can test whether the beta-convergence theory (Barro et al., 1991; Barro and Sala-iMartin, 1992) of a reduction in inequality by catching up of less developed with higher economic growth holds true for our setting.
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Topolewski, Ł., 2021. Relationship between Energy Consumption and Economic Growth in European Countries: Evidence from Dynamic Panel Data Analysis. Energies 14, 3565. https://doi.org/10.3390/en14123565 Vujanović, N., Radošević, S., Stojčić, N., Hisarciklilar, M., Hashi, I., 2022. FDI spillover effects on innovation activities of knowledge using and knowledge creating firms: Evidence from an emerging economy.