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Evaluation of firm R&D and innovation support: new indicators and the ex-ante prediction of ex-post additionality

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  • Bianca Potì
  • Giovanni Cerulli

Abstract

This paper presents a comprehensive logical framework for designing ex-post evaluation of corporate R&D and innovation (RDI) policies, which logically connects: actors' behaviour, indicators, ex-post (counterfactual) evaluation and ex-ante prediction of firm (R&D) additionality potential. The paper proposes two ways to improve on the methods currently used in the evaluation literature. On the one hand, it suggests some new indicators related to firm ‘additionality potential’ by looking at the conditions enabling increasing returns of R&D to public subsidies. On the other hand, it provides public agencies with a procedure — based on an econometric/counterfactual approach — to obtain an ex-ante synthetic measure of the (ex-post) R&D additionality capacity of firms, rooted in the literature on credit-risk assessment. We will discuss this method at length, also by highlighting its critical aspects. Copyright , Beech Tree Publishing.

Suggested Citation

  • Bianca Potì & Giovanni Cerulli, 2011. "Evaluation of firm R&D and innovation support: new indicators and the ex-ante prediction of ex-post additionality," Research Evaluation, Oxford University Press, vol. 20(1), pages 19-29, March.
    • Handle: RePEc:oup:rseval:v:20:y:2011:i:1:p:19-29
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    File URL: http://hdl.handle.net/10.3152/095820211X12941371876427
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    Cited by:

    1. Paredes-Frigolett, Harold & Pyka, Andreas & Leoneti, Alexandre Bevilacqua, 2021. "On the performance and strategy of innovation systems: A multicriteria group decision analysis approach," Technology in Society, Elsevier, vol. 67(C).
    2. Matteo Pedrini & Valentina Langella & Mario Alberto Battaglia & Paola Zaratin, 2018. "Assessing the health research’s social impact: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1227-1250, March.
    3. Giovanni Cerulli, 2012. "Are R&D Subsidies Provided Optimally? Evidence from a Simulated Agency-Firm Stochastic Dynamic Game," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-7.
    4. Dziallas, Marisa & Blind, Knut, 2019. "Innovation indicators throughout the innovation process: An extensive literature analysis," Technovation, Elsevier, vol. 80, pages 3-29.
    5. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).

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