Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach

Fenfen Wei, Nanping Feng and Kevin H. Zhang
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Fenfen Wei: School of Management, Hefei University of Technology, Hefei 230009, China
Nanping Feng: School of Management, Hefei University of Technology, Hefei 230009, China
Kevin H. Zhang: Department of Economics, Illinois State University, Normal, IL 61790-4200, USA

Sustainability, 2017, vol. 9, issue 7, 1-15

Abstract: Innovation talents, as a most active and important resource in innovation activities, are receiving increasing attention in the enhancement of innovation capability. It seems that areas with strong innovation capability are more attractive to innovation talents. To explore the impact of innovation capability—measured by innovation environment input efficiency—on the distribution of innovation talent, and given the heavy-tailed distribution of talents, a quantile regression approach is adopted for Chinese data covering 2001–2015. The results show that: (a) at the country level, the innovation environment and innovation talents are surprisingly negatively related due to pre-reform special regional strategies and the immature innovation environment in China, while both innovation input and efficiency facilitates the agglomeration of innovation talent; and (b) at the regional level, some different influences on talents appear: the strongest negative impact of the innovation environment is in the areas with a low level of talents, moderate positive effects of innovation input and efficiency can be seen in areas with a medium level of talents, and significantly positive contributions from innovation input and efficiency can be seen in the areas that already have a high level of talents. The results offer some suggestions for managers and the government, which are beneficial for the guidance of the ordered flow of innovation talents and the enhancement of regional innovation capability and sustainability.

Keywords: innovation talents; innovation capability; quantile regression; density forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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