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JACIII Vol.24 No.3 pp. 272-281
doi: 10.20965/jaciii.2020.p0272
(2020)

Paper:

A Study of the Impact of Innovation on Industrial Upgrading in China: A Spatial Econometric Analysis Based on China’s Provincial Panel Data

Jiao Jiang* and Xindong Zhao**,†

*School of Economics and Finance, Huaqiao University
No.269 Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China

**Institute of Quantitative Economics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

Received:
October 25, 2019
Accepted:
January 12, 2020
Published:
May 20, 2020
Keywords:
innovation, industrial structure upgrading, spatial Durbin model, spatial spillover effect
Abstract

This study provides an in-depth analysis of the impact of innovation on industrial upgrading from theoretical and practical perspectives. In terms of theory, based on the endogenous growth theory model, a multi-sectoral growth model is constructed to portray the inherent logical connection between innovation and industrial structure upgrading. The results show that innovation has an important impact on industrial structure upgrading. Industrial structure upgrading depends on differences in innovation level among industries and the substitution relationship of finished products in the industry. From an empirical perspective, based on the panel data of 31 provinces in China from 2005 to 2017, the spatial effect of innovation on industrial upgrading was analyzed using the spatial Durbin model. The results show that innovation and industrial structure upgrading has significant spatial correlation effects, and regional innovation can drive China’s industrial upgrading. Meanwhile, the space spillover effect is an important factor that cannot be ignored in industrial upgrading.

Moran scatter plots of industrial structure upgrading

Moran scatter plots of industrial structure upgrading

Cite this article as:
J. Jiang and X. Zhao, “A Study of the Impact of Innovation on Industrial Upgrading in China: A Spatial Econometric Analysis Based on China’s Provincial Panel Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.3, pp. 272-281, 2020.
Data files:
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Last updated on Apr. 22, 2024