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Efficiency of the R&D Sector in the EU-27 at the Regional Level: An Application of DEA

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  • Aristovnik, Aleksander

Abstract

The main aim of the paper is to measure the relative efficiency of the R&D sector in the EU-27 at the regional level. For this purpose, the paper applies a non-parametric approach, i.e. data envelopment analysis (DEA), to assess the relative technical efficiency of R&D activities across selected EU (NUTS-2) regions. The empirical analysis integrates available inputs (R&D expenditures, researchers and employment in high-tech sectors) and outputs (patent and high-tech patent applications) over the 2005–2010 period. The empirical results show that among regions with a high intensity of R&D activities the most efficient performers are Noord-Brabant (Netherlands), Stuttgart (Germany) and Tirol (Austria). In contrast, a wide range of NUTS-2 regions from the Baltics, Eastern and Southern Europe is characterized by an extremely low rate of knowledge production and its efficiency, particularly in Poland (Mazowieckie), Lithuania (Lietuva), Latvia (Latvija), Romania (Bucuresti-Ilfov), Bulgaria (Yugozapaden), Slovakia (Západné Slovensko), Greece (Attiki), Spain (Canarias) and Italy (Sardegna).

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  • Aristovnik, Aleksander, 2014. "Efficiency of the R&D Sector in the EU-27 at the Regional Level: An Application of DEA," MPRA Paper 59081, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:59081
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    References listed on IDEAS

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    1. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    2. Avkiran, Necmi K., 2001. "Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 35(1), pages 57-80, March.
    3. Henk F. Moed, 2002. "Measuring China"s research performance using the Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 281-296, March.
    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. Wei Meng & Zhenhua Hu & Wenbin Liu, 2006. "Efficiency evaluation of basic research in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 85-101, October.
    6. Zhong, Wei & Yuan, Wei & Li, Susan X. & Huang, Zhimin, 2011. "The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data," Omega, Elsevier, vol. 39(4), pages 447-455, August.
    7. Jiancheng Guan & Nan Ma, 2004. "A comparative study of research performance in computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 339-359, November.
    8. Liu, John S. & Lu, Wen-Min, 2010. "DEA and ranking with the network-based approach: a case of R&D performance," Omega, Elsevier, vol. 38(6), pages 453-464, December.
    9. Seema Sharma & V. J. Thomas, 2008. "Inter-country R&D efficiency analysis: An application of data envelopment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 483-501, September.
    10. Karkazis, John & Thanassoulis, Emmanuel, 1998. "Assessing the effectiveness of regional development policies in Northern Greece using data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 32(2), pages 123-137, June.
    11. Jiancheng Guan & Kaihua Chen, 2010. "Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 165-173, January.
    12. Aleksander Aristovnik, 2011. "The relative efficiency of education and R&D expenditures in the new EU member states," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(5), pages 832-848, August.
    13. Can Huang & Celeste Amorim Varum & Joaquim Borges Gouveia, 2006. "Scientific productivity paradox: The case of China's S&T system," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 449-473, November.
    14. Lee, Hakyeon & Park, Yongtae & Choi, Hoogon, 2009. "Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach," European Journal of Operational Research, Elsevier, vol. 196(3), pages 847-855, August.
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    Cited by:

    1. Dejan Ravšelj & Aleksander Aristovnik, 2018. "The Impact of Private Research and Development Expenditures and Tax Incentives on Sustainable Corporate Growth in Selected OECD Countries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    2. Tihana Škrinjarić, 2020. "R&D in Europe: Sector Decomposition of Sources of (in)Efficiency," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    3. Martina Halaskova & Beata Gavurova & Kristina Kocisova, 2020. "Research and Development Efficiency in Public and Private Sectors: An Empirical Analysis of EU Countries by Using DEA Methodology," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    4. Dejan Ravšelj & Aleksander Aristovnik, 2017. "R&D Subsidies as Drivers of Corporate Performance in Slovenia: The Regional Perspective," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 2, pages 79-95, June.
    5. Kadri Männasoo & Jaanika Meriküll, 2015. "The impact of firm financing constraints on R&D over the business cycle," Working Papers 348, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    6. Aneta Masternak‐Janus, 2022. "Measuring the efficiency of materials management based on data envelopment analysis approach: the case of Polish regions," Papers in Regional Science, Wiley Blackwell, vol. 101(3), pages 603-618, June.
    7. Männasoo, Kadri & Meriküll, Jaanika, 2020. "Credit constraints and R&D over the boom and bust: Firm-level evidence from Central and Eastern Europe," Economic Systems, Elsevier, vol. 44(2).
    8. Luh, Yir-Hueih & Jiang, Wun-Ji & Huang, Szu-Chi, 2016. "Trade-related spillovers and industrial competitiveness: Exploring the linkages for OECD countries," Economic Modelling, Elsevier, vol. 54(C), pages 309-325.
    9. Beáta Gavurová & Martina Halásková & Samuel Koróny, 2019. "Research and Development Indicators of EU28 Countries from Viewpoint of Super-efficiency DEA Analysis," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(1), pages 225-242.

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    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Efficiency; EU; NUTS-2 regions; R&D;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics

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