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A new approach to estimation of the R&D–innovation–productivity relationship

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  • Christopher F. Baum
  • Hans Lööf
  • Pardis Nabavi
  • Andreas Stephan

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.

Suggested Citation

  • Christopher F. Baum & Hans Lööf & Pardis Nabavi & Andreas Stephan, 2017. "A new approach to estimation of the R&D–innovation–productivity relationship," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 26(1-2), pages 121-133, February.
  • Handle: RePEc:taf:ecinnt:v:26:y:2017:i:1-2:p:121-133
    DOI: 10.1080/10438599.2016.1202515
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    References listed on IDEAS

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    1. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    2. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    3. Martin Andersson & Hans Lööf, 2009. "Learning‐by‐Exporting Revisited: The Role of Intensity and Persistence," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(4), pages 893-916, December.
    4. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
    5. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "Generalized multilevel structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 167-190, June.
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    Cited by:

    1. Castelnovo, Paolo & Florio, Massimo & Forte, Stefano & Rossi, Lucio & Sirtori, Emanuela, 2018. "The economic impact of technological procurement for large-scale research infrastructures: Evidence from the Large Hadron Collider at CERN," Research Policy, Elsevier, vol. 47(9), pages 1853-1867.
    2. Marcel Ausloos & Francesca Bartolacci & Nicola G. Castellano & Roy Cerqueti, 2018. "Exploring how innovation strategies at time of crisis influence performance: a cluster analysis perspective," Papers 1808.05893, arXiv.org.
    3. Eric J. Bartelsman & Martin Falk & Eva Hagsten & Michael Polder, 2019. "Productivity, technological innovations and broadband connectivity: firm-level evidence for ten European countries," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 25-48, March.
    4. David B. Audretsch & Marian Hafenstein & Alexander S. Kritikos & Alexander Schiersch, 2018. "Firm Size and Innovation in the Service Sector," Discussion Papers of DIW Berlin 1774, DIW Berlin, German Institute for Economic Research.

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