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The innovation efficiency of German regions - a shared-input DEA approach

Author

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  • Tom Broekel

    (Leibnitz-University Hannover)

  • Nicky Rogge

    (Katholieke Universiteit Leuven)

  • Thomas Brenner

    (Philipps University Marburg)

Abstract

The paper contributes to the debate on how to measure regions’ innovation performance. On the basis of the concept of regional innovation efficiency, we propose a new measure that eases the issue of choosing between industry-specific or global measures. We argue for the use of a robust shared-input DEA-model to estimate regions’ innovation efficiency in a global manner, while it can be disaggregated into industry-specific innovation efficiency measures. The latter is particularly useful when relating the innovative output to the R and D input involves the use of blurry matching procedures. We illustrate the use of the method by investigating the innovation efficiency as well as its change in time of German labor market regions. It is shown that the method treats regions that have industry structures skewed towards industries with high and low innovation intensities more fairly than traditional approaches.

Suggested Citation

  • Tom Broekel & Nicky Rogge & Thomas Brenner, 2013. "The innovation efficiency of German regions - a shared-input DEA approach," Working Papers on Innovation and Space 2013-08, Philipps University Marburg, Department of Geography.
    • Handle: RePEc:pum:wpaper:2013-08
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    References listed on IDEAS

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    2. Aguilar-Barceló, José G. & Higuera-Cota, Fernanda, 2019. "Challenges in innovation management for Latin America and the Caribbean: an efficiency analysis," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    3. Yamin Du & Wonchul Seo, 2022. "A Comparative Study on the Efficiency of R&D Activities of Universities in China by Region Using DEA–Malmquist," Sustainability, MDPI, vol. 14(16), pages 1-13, August.
    4. Panpan Liu & Guanghui Han & Haichao Yang & Xiaobo Li, 2024. "A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
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    6. Bischoff, Thore Sören & Hipp, Ann & Runst, Petrik, 2022. "Firm innovation and generalized trust as a regional resource," ifh Working Papers 32/2022, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    7. Prokop, Viktor & Hajek, Petr & Stejskal, Jan, 2021. "Configuration Paths to Efficient National Innovation Ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    8. Xiangyu Guo & Canhui Deng & Dan Wang & Xu Du & Jiali Li & Bowen Wan, 2021. "International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    9. Stepan Zemtsov & Maxim Kotsemir, 2019. "An assessment of regional innovation system efficiency in Russia: the application of the DEA approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 375-404, August.

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    Keywords

    Keywords: regional innovation efficiency; shared-input DEA; nonparametric efficiency analysis; regional innovation.;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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