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Who leads research productivity growth? Guidelines for R&D policy-makers

Author

Listed:
  • Fernando Jiménez-Sáez

    (Universidad Politécnica de Valencia)

  • Jon Mikel Zabala-Iturriagagoitia

    (Lund University)

  • Jose Luis Zofío

    (Universidad Autónoma de Madrid)

Abstract

This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).

Suggested Citation

  • Fernando Jiménez-Sáez & Jon Mikel Zabala-Iturriagagoitia & Jose Luis Zofío, 2013. "Who leads research productivity growth? Guidelines for R&D policy-makers," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 273-303, January.
  • Handle: RePEc:spr:scient:v:94:y:2013:i:1:d:10.1007_s11192-012-0763-0
    DOI: 10.1007/s11192-012-0763-0
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    3. Amer Ait Sidhoum, 2023. "Measuring farm productivity under production uncertainty," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 672-687, October.
    4. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2018. "Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences," Journal of Informetrics, Elsevier, vol. 12(3), pages 784-805.
    5. Xi Qin & Xiaoling Wang & Yusen Xu & Yawen Wei, 2019. "Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry," Sustainability, MDPI, vol. 11(4), pages 1-11, February.

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    More about this item

    Keywords

    Science and technology policy; Policy evaluation; Malmquist productivity index; Data envelopment analysis;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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