A new approach to estimation of the R&D–innovation–productivity relationship. Issue 1 (17th February 2017)

• Record Type: Journal Article
• Authors: Baum, Christopher F.; Lööf, Hans; Nabavi, Pardis; Stephan, Andreas
• Is Part Of: Economics of innovation and new technology. Volume 26:Issue 1/2(2017)
• Page Start: 121
• Page End: 133
• Publication Date: 2017-02-17
• Subjects: R&D -- innovation -- productivity -- generalized structural equation model -- community innovation survey; C23 -- L6 -- O32 -- O52; Technological innovations -- Economic aspects -- Periodicals; 338.06405
• DOI: 10.1080/10438599.2016.1202515
• Languages: English
• ISSNs: 1043-8599

Abstract

ABSTRACT: We apply a generalized structural equation model approach to the estimation of the relationship between R&D, innovation and productivity that focuses on the potentially crucial heterogeneity across sectors. The model accounts for selectivity and handles the endogeneity of this relationship in a recursive framework which allows for feedback effects from productivity to future R&D investment. Our approach enables the estimation of the different equations as one system, allowing the coefficients to differ across sectors, and also permits us to take cross-equation correlation of the errors into account. Employing a panel of Swedish manufacturing and service firms observed in three consecutive Community Innovation Surveys in the period 2008–2012, our full-information maximum likelihood estimates show that many key channels of influence among the model's components vary meaningfully in their statistical significance and magnitude across six different sectors based on the OECD classification on technological and knowledge intensity. These results cast doubt on earlier research which does not allow for sectoral heterogeneity.