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

Authors: Fenfen Wei; Nanping Feng; Kevin Zhang;

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

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.

Related Organizations
Keywords

density forecast, quantile regression, Environmental effects of industries and plants, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, innovation capability, innovation talents; innovation capability; quantile regression; density forecast, GE1-350, innovation talents

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Average
gold