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Distributions of scientific funding across universities and research disciplines

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  • Wu, Jiang

Abstract

Distributing scientific funding to the suitable universities and research fields is very important to the innovation acceleration in science and technology. Using a longitudinal panel dataset of the National Natural Science Foundation of China (NSFC), the total 224,087 sponsored projects is utilized to investigate the distributions of scientific funding across universities and research disciplines. The inequality of funding distribution is studied through the investigation of Gini coefficient, and its fundamental rules are discovered through the technique of distribution fitting. It is found that the inequality of distributions of NSFC funding across 1971 universities is decreasing, and the distribution of funding and supported universities of 971 research fields follow Generalized Pareto distribution and Geometric distribution function, respectively. This study is dedicated to give an entire landscape to help make policy of distributing scientific funding.

Suggested Citation

  • Wu, Jiang, 2015. "Distributions of scientific funding across universities and research disciplines," Journal of Informetrics, Elsevier, vol. 9(1), pages 183-196.
  • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:183-196
    DOI: 10.1016/j.joi.2014.12.007
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    1. Auranen, Otto & Nieminen, Mika, 2010. "University research funding and publication performance--An international comparison," Research Policy, Elsevier, vol. 39(6), pages 822-834, July.
    2. Payne A. Abigail & Siow Aloysius, 2003. "Does Federal Research Funding Increase University Research Output?," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 3(1), pages 1-24, May.
    3. Sotaro Shibayama, 2011. "Distribution of academic research funds: a case of Japanese national research grant," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 43-60, July.
    4. Paul J Roebber & David M Schultz, 2011. "Peer Review, Program Officers and Science Funding," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-6, April.
    5. Corie Lok, 2010. "Science funding: Science for the masses," Nature, Nature, vol. 465(7297), pages 416-418, May.
    6. Dorfman, Robert, 1979. "A Formula for the Gini Coefficient," The Review of Economics and Statistics, MIT Press, vol. 61(1), pages 146-149, February.
    7. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2013. "The impact of unproductive and top researchers on overall university research performance," Journal of Informetrics, Elsevier, vol. 7(1), pages 166-175.
    8. Zhang, Han & Patton, Donald & Kenney, Martin, 2013. "Building global-class universities: Assessing the impact of the 985 Project," Research Policy, Elsevier, vol. 42(3), pages 765-775.
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    Cited by:

    1. Chen, Zhuo & Yang, Zhenbing & Yang, Lili, 2020. "How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    2. Yuret, Tolga, 2016. "Interfield equality: Journals versus researchers," Journal of Informetrics, Elsevier, vol. 10(4), pages 1196-1206.
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    5. Qiang Zhi & Tianguang Meng, 2016. "Funding allocation, inequality, and scientific research output: an empirical study based on the life science sector of Natural Science Foundation of China," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 603-628, February.
    6. Zhao, Star X. & Tan, Alice M. & Yu, Shuang & Xu, Xin, 2018. "Analyzing the research funding in physics: The perspective of production and collaboration at institution level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 662-674.
    7. Joonha Jeon & So Young Kim, 2018. "Is the gap widening among universities? On research output inequality and its measurement in the Korean higher education system," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 589-606, March.
    8. Wu, Dengsheng & Yuan, Lili & Li, Ruoyun & Li, Jianping, 2018. "Decomposing inequality in research funding by university-institute sub-group: A three-stage nested Theil index," Journal of Informetrics, Elsevier, vol. 12(4), pages 1312-1326.
    9. Jianping Li & Yongjia Xie & Dengsheng Wu & Yuanping Chen, 2017. "Underestimating or overestimating the distribution inequality of research funding? The influence of funding sources and subdivision," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 55-74, July.

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