Abstract

The aim of this study is to investigate the relationship between competition and innovation through the knowledge spillover effect. In particular, we investigate whether R&D competition is sensitive to economic shock. To this end, we consider a period of time related also to the 2008 financial crisis. We implement an empirical analysis of 879 worldwide R&D-intensive firms. In order to measure technological proximity, we use two approaches: one based on Jaffe industry weight matrix, relative to patents distributed across technology classes; one based on trade intensity between sectors using input–output matrix data. The empirical results show a positive effect of R&D externalities on competitive interactions before the beginning of crisis and a negative one after it. These findings are robust with respect to the procedure employed in the estimation method.

Highlights

  • This paper aims to examine the relation between research and development (R&D) competitions on firm’s own R&D efforts

  • With respect to the existing literature, our study provides a twofold contribution: on the one hand, we investigate whether R&D competition is sensitive to financial shocks

  • We explore the competition effect by implementing an alternative empirical procedure based on trade intensity between sectors using input–output matrix data (Timmer, Dietzenbacher, Los, Stehrer & de Vries., 2015)

Read more

Summary

Introduction

This paper aims to examine the relation between R&D competitions on firm’s own R&D efforts. The analysis covers the period 2002–2010 and it is performed in two stages. With respect to the existing literature, our study provides a twofold contribution: on the one hand, we investigate whether R&D competition is sensitive to financial shocks. To this end, we split our sample into two periods, one before the beginning of the crisis (2002-2006), and one after it (2007-2010). We explore the competition effect by implementing an alternative empirical procedure based on trade intensity between sectors using input–output matrix data (Timmer, Dietzenbacher, Los, Stehrer & de Vries., 2015).

Literature Review
Data and Variables
GMM Estimation Procedure
Robustness Analysis
Policy Implications and Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.