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Public R&D support and firm performance: A multivariate dose-response analysis

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  • Nilsen, Øivind A.
  • Raknerud, Arvid
  • Iancu, Diana-Cristina

Abstract

We analyse all the major sources of direct and indirect research and development (R&D) support to the business enterprise sector in a single country, Norway, for the period 2002–2013, treating the financial support for R&D from several instruments as a multivariate dose exposure. The output additionality of support to incumbent firms that regularly perform R&D (R&D-incumbents), which obtain about 65 per cent of all R&D support to business enterprises, is insignificant for any instrument or policy mixture. However, the estimated additionality of support to R&D-starters (firms without prior R&D activity), which obtain about 30 per cent of all R&D support, is generally positive. In this firm category, the main instruments for direct R&D support in Norway generate significantly less output and economic activity per NOK 1 million in support than do tax credits, despite the fact these instruments manage large project portfolios at considerable administrative costs. We do not identify positive effects of R&D support on labour productivity or the return on assets for any of the instruments. Our main policy implication is that R&D instruments for the business enterprise sector should be designed in favour of R&D-starters over R&D-incumbents, that is, shifting the focus from the intensive to the extensive margin.

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  • Nilsen, Øivind A. & Raknerud, Arvid & Iancu, Diana-Cristina, 2020. "Public R&D support and firm performance: A multivariate dose-response analysis," Research Policy, Elsevier, vol. 49(7).
    • Handle: RePEc:eee:respol:v:49:y:2020:i:7:s0048733320301451
    DOI: 10.1016/j.respol.2020.104067
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    Cited by:

    1. Mulligan, Kevin & Lenihan, Helena & Doran, Justin & Roper, Stephen, 2022. "Harnessing the science base: Results from a national programme using publicly-funded research centres to reshape firms’ R&D," Research Policy, Elsevier, vol. 51(4).
    2. Hyensup Shim & Kiyoon Shin, 2021. "Empirical Analysis of Evidence-Based Policymaking in R&D Programmes," Sustainability, MDPI, vol. 14(1), pages 1-15, December.
    3. Lenihan, Helena & Mulligan, Kevin & Doran, Justin & Rammer, Christian & Ipinnaiye, Olubunmi, 2022. "R&D grant and tax credit support for foreign-owned subsidiaries: Does it pay off?," ZEW Discussion Papers 22-003, ZEW - Leibniz Centre for European Economic Research.
    4. Nilsen, Øivind A. & Raknerud, Arvid, 2022. "Dynamics of First-Time Patenting Firms," Discussion Paper Series in Economics 11/2022, Norwegian School of Economics, Department of Economics.
    5. Kim Kiman & Yu Jongmin, 2022. "Linear or Nonlinear? Investigation an Affect of Public Subsidies on SMEs R&D Investment," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2519-2546, September.
    6. Huang, Dan & Liu, Baohua & Chan, Kam C. & Chen, Yining, 2023. "Intended and unintended effects of mandatory R&D disclosure on innovation outcomes," Economic Modelling, Elsevier, vol. 119(C).
    7. Ion POPA & Cezar-Petre SIMION & Catalina Florentina ALBU, 2020. "Gap Analysis Of The Resources And Results Of The National Research-Development System From The Perspective Of Technology Transfer Processes," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 436-444, November.
    8. Rituparna Kaushik & Sourabh Bikas Paul, 2022. "Do Competition Improve Persistence in Innovation Effort? Sectoral Patterns and Evidence from India," Journal of Industry, Competition and Trade, Springer, vol. 22(2), pages 259-296, June.
    9. Geetha, Selvaraj & Jeon, JeongHwan, 2023. "Stratified network mapping decision making technique based decision support framework for R&D budget allocation in South Korea," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    10. Lenihahn, Helena & Mulligan, Kevin & Perez-Alaniz, Mauricio & Rammer, Christian, 2023. "Serving the right menu of R&D policy instruments to firms: An analysis of policy mix sequencing," ZEW Discussion Papers 23-009, ZEW - Leibniz Centre for European Economic Research.
    11. Dai, Xiaoyong & Chapman, Gary, 2022. "R&D tax incentives and innovation: Examining the role of programme design in China," Technovation, Elsevier, vol. 113(C).

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    More about this item

    Keywords

    R&D; Public policy instruments; Firm performance; Multivariate dose-response; Treatment effects; Matching; Output additionality;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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