Assessing measurement errors in the CDM research–innovation–productivity relationships. Issue 1 (17th February 2017)

• Record Type: Journal Article
• Authors: Mairesse, Jacques; Robin, Stéphane
• Is Part Of: Economics of innovation and new technology. Volume 26:Issue 1/2(2017)
• Page Start: 93
• Page End: 107
• Publication Date: 2017-02-17
• Subjects: Measurement errors -- classical errors-in-variables -- attenuation bias -- R&D -- innovation and productivity; C18 -- C23 -- C33 -- O30 -- O31 -- O33; Technological innovations -- Economic aspects -- Periodicals; 338.06405
• DOI: 10.1080/10438599.2016.1210771
• Languages: English
• ISSNs: 1043-8599

Abstract

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.