Author(s)

Shujing Guo

School College of Economics, Jinan University, Guangzhou, China.

Based on the Entropy Method, ESDA and spatial panel data model methods using urban patents database of China’s 285 cities during 2014-2015, this article explored the spatial pattern and determinants of innovative output. The results show that: Innovative output shows obvious characteristics of geographical agglomeration and spatial agglomeration. Most cities had strong path dependence and lock-in characteristics, and transition didn’t occur. When the inter-city correlation is considered, human capital and industrial base are the major driving forces for boosting innovation output. The input of innovation elements will not only promote the improvement of local innovation ability, but also promote the development of innovation capability in the neighboring cities.

Innovation Output, Spatial Autocorrelation, Spatial Spillover Effects, Spatial Dynamic Panel Data

Guo, S. (2019) Spatial-Temporal Characteristics and Determinants of Innovation Output in China. Modern Economy, 10, 12-28. doi: 10.4236/me.2019.101002.