Does digital economy development promote innovation efficiency? A spatial econometric approach for Chinese regions

ABSTRACT

In the context of the rapid development of the digital economy, it’s an important topic how to play the role of digital technology in improving innovation efficiency. Employing the spatial econometric model with province-level panel data during 2006–2018, the article explores the impact of the development of the digital economy on innovation efficiency in China. The analysis unveils three major findings. First, the innovation efficiency has significant positive spatial externalities and the digital economy has significantly positive direct effects and spatial spillover effects on innovation efficiency, but the above effects are heterogeneous for different regions and innovation subjects. Second, the impact of digital economy development on innovation efficiency has characteristics of a certain degree of lag effect and continuity. Third, the threshold effect analysis reveals the non-linear characteristic of the increasing marginal effect of the digital economy on innovation efficiency. Altogether, the development of the digital economy has become an important driving force for promoting China’s innovation efficiency. The findings of this paper provide empirical evidence for understanding the relationship between the digital economy development and innovation efficiency, giving significant implications for the innovative development of developing countries.

KEYWORDS:

• Digital economy
• digitization
• innovation efficiency
• spatial spillover

JEL CLASSIFICATION:

• O3
• R11

Disclosure statement

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

Notes

1 In 2019, the number of R&D personnel in China reached 240,000, ranking first in the world; R&D expenditure reached $223.817 billion, accounting for 2.1% of GDP, second in the world; 59,000 patent applications were filed through the Patent Cooperation Treaty, surpassing the United States to rank first in the world for the first time.

2 Although the spatial Durbin model (SDM) is a more general model which contains the endogenous interaction effects among the dependent variable and the exogenous interaction effects among the independent variables, but relevant statistical tests do not all support the use of SDM in the model regressions in the paper. Thus, following the practice of Elhorst (2014), we adopt a simpler form of spatial econometric model – SLM – for empirical estimation to take into account the comparability of empirical results and avoid the overfitting problem of the model. In addition, it should be note that according to the theory of spatial econometrics, SLM also includes the exogenous interaction effects among the independent variables, but the difference between SLM and SDM is that the spatial effects of SLM is conducted through the endogenous variable. Lastly, we applied the Hausman test to decide whether to apply a random effect model or fixed effect model. The Hausman test confirms that the choice of fixed effects is adequate in this setting.

3 They include four direct-controlled municipalities, 21 provinces and four autonomous regions. Tibet, Hainan, Hong Kong, Macau and Taiwan are excluded here due to data missing problem during the whole study period.

4 In theory, the development of the digital economy lagged by three periods may also have impacts on the innovation efficiency of the current period, but the multicollinearity test shows that there will be serious multicollinearity problems (the maximum VIF of the parameter reaches 40.1) when being included in the model. Thus, we only include two lags of core explanatory variables for regressions with reference to research of Sukumar et al. (2020).

5 According to the 45rd China Statistical Report on Internet Development issued by CNNIC, the number of Internet users in China has soared from 111 million in 2005 – 904 million in 2019. Internet penetration rate increased from 8.5% in 2005 to 64% in 2019.

6 The eastern coastal regions in China are Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong. The central and western regions are Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

7 For instance, among the 29 regions in 2018, the region with the highest Internet penetration rate was Beijing, reaching 75%, and the lowest region was Yunnan, with only 43%.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 72074096], Project of Guangdong Industrial Development and Guangdong-Hong Kong-Macao-Taiwan Regional Cooperation Research Center [grant number 37714001004].

Notes on contributors

Peng Wang

Peng Wang is a professor in the School of Economics of Jinan University. His research interests focus on regional economics and sustainable development.

Cong Cen

Cong Cen is a PhD candidate in the School of Economics of Jinan University. His research interests are spatial spillover effects of economic activities and regional innovation management.