The nexus of intellectual property rights and firm productivity in China: A nonlinear approach

Abstract

This paper explores the way that intellectual property rights (IPRs) protection as a moderator variable affects firm-level productivity gains through innovation (filing patents) and diffusion (importing goods). Applying the estimation method of conditional average treatment effect by Lee, Okui, and Whang (2017) to Chinese firm data, I show the graphic representation of the non-linearity that characterizes the heterogeneous average treatment effects of patenting and importing activities on firm productivity. The graphic results suggest that the productivity effect of patenting is positively influenced by local IPRs protection, while that of importing is initially negatively and subsequently positively affected by IPRs protection. I also find that overstrict protection of IPRs may diminish the productivity impact of both channels, and private-owned firms rely more on IPRs protection to enhance productivity through filing patents.

Keywords:

• Intellectual property rights
• firm productivity
• conditional average treatment effect

JEL Classifications:

• F43
• O31
• O34
• O43
• P26

Acknowledgments

I am indebted to Thanasis Stengos and Yiguo Sun for their patient guidance. I would like to thank Tibor Besedes (Editor), Charles Van Marrewijk (Editor), Feng Chen, Dandan Wang, two anonymous referees, and participants at Canadian Economics Association Annual Conference for their helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 See, for instance, Ang, Cheng, and Wu (2014), Fang, Lerner, and Wu (2017), Hu and Yin (2022), Lin, Lin, and Song (2010), Lu, Png, and Tao (2013), and Lai, Maskus, and Yang (2020).

2 Institutions are important as well from a spatial perspective, as suggested by Liu, Stengos, and Sun (2024).

3 This method is favored for its ability to address endogeneity resulting from the self-selection of firms in international trade and innovation as more productive firms are more likely to engage in international trade and innovation.

4 Note that the terms ‘region’ and ‘province’ are used interchangeably in this study.

5 The availability of customs data from China imposes limitations on the sample period for this study.

6 The annual NERI score of IPRs is calculated from two components. The first one is the ratio of the number of applications for patent to the size of engineers, and the second one is the ratio of the number of approved patent applications to the size of engineers.

7 According to Ang, Cheng, and Wu (2014), IP protection law came into effect in China after 1980, encompassing patent law, trademark law, and copyright law.

8 See e.g. http://www.sz.gov.cn/en_szgov/laws/content/post_8860163.html for Regulations of Shenzhen Special Economic Zone on Protection of Intellectual Property Rights adopted by the People's Congress of Shenzhen Municipality.

9 The index used in this paper covers the years of 2002, 2004, 2005, and 2006.

10 In other words, a firm will be labeled as a patenting (importing) firm as long as it has applied at least for one patent (imported goods at least for once) over the past years.

11 Following the literature, the two treatments (i.e. importing and patenting) are estimated separately. However, it is recognized that such way of dealing with the two treatments may induce technical challenge in econometrics.

12 See Dang and Motohashi (2015) and Li (2012) for further discussion on the patent subsidy programs in China.

13 The findings are in general robust to the use of IPRs measure obtained from the Urban Competitiveness Report Blue Book of CASS following Fang, Lerner, and Wu (2017), especially that the upward relationship between the IPRs and productivity return of patenting remains true. Due to space limitation, I did not report the detailed results of the CASS measure of IPRs in the paper, which is available from the author upon request.

14 It is not feasible to estimate effects separately for all types of firm ownership due to the large data requirements for estimating conditional average treatment effects.

15 Note that the sample size used in the CATE estimation would slightly affect the range of moderator variable in the local linear estimation to ensure the smoothness.

16 State-owned firms in China are usually owned by the governments through the State-owned Assets Supervision and Administration Commission (SASAC).

17 The findings are in general robust to the use of IPRs measure obtained from the Urban Competitiveness Report Blue Book of CASS, especially that firms in private sector tend to benefit more from the improvement of institutional quality.