The impact of R&D intensity on firm performance in an emerging market: Evidence from China’s electronics manufacturing firms

Abstract

The impact pattern of R&D intensity on firm performance is an interesting but not explicitly untangled topic in the emerging market. This study explores the topic in the context of China using a unique unbalanced panel data set for the China’s electronics manufacturing firms. In order to strengthen the robustness of the statistical results, this study employs two coherent models: pooled-OLS regression and pooled-quantile regression techniques. The pooled-OLS regressions focusing on mean effects present the robust evidence of a positive relationship between R&D intensity on firm performance, and the further pooled-quantile regressions portraying the relative importance of explanatory variables at different points of the performance distribution show that the R&D intensity exhibits a stronger impact on the better-performance firms. Our study presents some new evidence for the impact of R&D intensity on firm performance in the emerging market.

Keywords:

• R&D intensity
• firm performance
• China’s electronic manufacturing firms
• pooled-OLS regression
• pooled-quantile regression

Acknowledgements

The authors are very grateful for the insightful comments and suggestions of two anonymous reviewers and the Editors, which significantly improved this article.

Notes

1. For more details please refer to IRI website: http://iri.jrc.ec.europa.eu.

2. For more details please refer to Xinhua website：http://www.xinhuanet.com/politics/2016lh/zhibo/20160310b/wzsl.htm.

3. For more details please refer to UNCTAD website: http://unctad.org/en/Pages/Statistics.aspx.

4. For more details please refer to Chinese government’s website: http://www.gov.cn/xinwen/2016-05/16/content_5073622.htm

5. China’s Ministry of Industry and Information Technology implements the annual ranking of the top Chinese 100 manufacturing firms, sponsored in 1987, based on the scale in sale income with a special consideration of R&D investment, IT investment and export scale.

6. http://www.miit.gov.cn/n11293472/n11293832/n11293907/n11368223/13245091.html.

7. As a rule of thumb, if the VIF of a variable exceeds 10, that variable is said to be highly collinear (Gujarati 2003).

8. Gibra’s law, sometimes called Gibrat’s rule of proportionate growth is a rule defined by Gibrat (1931) stating that the size of a firm and its growth rate are independent.

9. For more details please refer to IRI website: http://iri.jrc.ec.europa.eu.

Additional information

Funding

The work described in this paper was funded by the National Natural Science Foundation of China (Project No. 71233003; 71471170), the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Project No. 2015131), the National Soft Science Research Program of China (Project No. 2013GXS4B087), and the Research Team of Natural Science Foundation of Guangdong Province in China (Project No. 2016A030312005).