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Funding allocation, inequality, and scientific research output: an empirical study based on the life science sector of Natural Science Foundation of China

Published on Feb 1, 2016in Scientometrics2.77
· DOI :10.1007/s11192-015-1773-5
Qiang Zhi2
Estimated H-index: 2
(CUFE: Central University of Finance and Economics),
Tianguang Meng5
Estimated H-index: 5
(THU: Tsinghua University)
Abstract
Scientific research activity produces the "Matthew Effect" on resource allocation. Based on a data set in the life sciences field from the National Natural Science Foundation of China (NSFC) during the 11th Five-Year-Plan (2006---2010), this paper makes an empirical study on how the Matthew Effect of funding allocation at the institutional level and city level impact scientific research activity output. With Gini coefficient evaluation, descriptive statistic analysis, and the Poisson regression model, we found that there has been a rapid increase in the concentration degree of funding allocation among institutions and cities. Within a period of 5 years, the Gini coefficients of total funding of institutions and cities as the units of measurement have increased from 0.61 and 0.74 to 0.67 and 0.79 respectively. However, this concentration in funding allocation did not result in significant additional benefits. Institutions awarded with more funding did not produce the expected positive spillover effect on their scientific research activity output. Instead, an "inverted U-shape" pattern of decreasing returns to scale was discovered, under which there was a negative effect on internal scientific research activity in the majority of institutions with concentrated funding allocation. Meanwhile, the result shows that Young Scholars projects under the NFSC produced high-level output. We conclude the study by discussing the possible reasons of the inverted U-shape pattern and its policy implications.
  • References (37)
  • Citations (9)
References37
Newest
#1Qiang Zhi (THU: Tsinghua University)H-Index: 5
#2Jun Su (THU: Tsinghua University)H-Index: 9
Last.Laura Diaz Anadon (Harvard University)H-Index: 22
view all 4 authors...
Cited By9
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#1Margit Osterloh (UZH: University of Zurich)H-Index: 25
#2Bruno S. Frey (UZH: University of Zurich)H-Index: 88
#1Fei Shu (CAS: Chinese Academy of Sciences)H-Index: 4
#2Charles-Antoine Julien (McGill University)H-Index: 5
Last.Vincent Larivière (UdeM: Université de Montréal)H-Index: 38
view all 0 authors...
#1Dengsheng Wu (CAS: Chinese Academy of Sciences)
#2Lili Yuan (CAS: Chinese Academy of Sciences)H-Index: 1
Last.Jianping Li (CAS: Chinese Academy of Sciences)
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#1Zhifeng Yin (CUFE: Central University of Finance and Economics)H-Index: 1
#2Zheng Liang (THU: Tsinghua University)H-Index: 1
Last.Qiang Zhi (CUFE: Central University of Finance and Economics)H-Index: 1
view all 3 authors...
#1Jianping LiXiaolei (CAS: Chinese Academy of Sciences)H-Index: 19
#2Yongjia Xie (CAS: Chinese Academy of Sciences)H-Index: 4
Last.Yuanping Chen (CAS: Chinese Academy of Sciences)H-Index: 1
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