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Assessing measurement errors in the CDM research–innovation–productivity relationships

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  • Jacques Mairesse
  • Stéphane Robin

Abstract

The Crépon-Duguet-Mairesse 1998 article, known as CDM, initiated a structural econometric framework to analyze the relationships among research, innovation and productivity, which has been estimated most generally on the basis of cross-sectional innovation survey-type data. Some econometric implementations of the CDM approach have suggested that such data give useful but imprecise measures of the innovation output (share of innovative sales), and to a lesser degree of the innovation input (R&D). These ‘measurement errors’ may result in attenuation biases of the estimated R&D and innovation impact elasticities in the two basic CDM ‘roots’ relations of R&D to innovation and innovation to productivity, as well as in the extended production function à la Griliches linking directly R&D to productivity. Using a panel of three waves of the French Community Innovation Survey (CIS), we assess these biases and the magnitude of the underlying measurement errors, assuming mainly that they are ‘white noise’ errors. We do so by comparing two pairs of usual panel estimators (Total and Between) in both the cross-sectional and time dimensions of the data (Levels and Differences). We find large measurement errors on innovation output in the innovation–productivity equation, resulting in large attenuation biases in the related elasticity parameter. We also find smaller but sizeable measurement errors on R&D, with significant attenuation biases in the corresponding elasticity estimates, in the R&D–innovation equation and the extended production function. Simulations suggest that the measurement errors on innovation and R&D are unaffected by similar measurement errors on the capital variable.

Suggested Citation

  • Jacques Mairesse & Stéphane Robin, 2017. "Assessing measurement errors in the CDM research–innovation–productivity relationships," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 26(1-2), pages 93-107, February.
  • Handle: RePEc:taf:ecinnt:v:26:y:2017:i:1-2:p:93-107
    DOI: 10.1080/10438599.2016.1210771
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    Cited by:

    1. Cristiano Antonelli & Christophe Feder, 2021. "Knowledge appropriability and directed technological change: the Schumpeterian creative response in global markets," The Journal of Technology Transfer, Springer, vol. 46(3), pages 686-700, June.
    2. Cristiano Antonelli & Christophe Feder, 2022. "Knowledge properties and the creative response in the global economy: European evidence for the years 1990–2016," The Journal of Technology Transfer, Springer, vol. 47(2), pages 459-475, April.
    3. Novaresio, Anna & Patrucco, Pier Paolo, 2023. "Innovation and trade in the automotive industry: evidence from European countries (1990-2018)," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202306, University of Turin.
    4. Cristiano Antonelli & Christophe Feder, 2021. "The Schumpeterian creative response: export and innovation: evidence for OECD countries 1995–2015," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(3), pages 803-821, October.
    5. Mohnen, Pierre, 2019. "R&D, innovation and productivity," MERIT Working Papers 2019-016, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Gaglio, Cyrielle & Kraemer-Mbula, Erika & Lorenz, Edward, 2022. "The effects of digital transformation on innovation and productivity: Firm-level evidence of South African manufacturing micro and small enterprises," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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