|
|
|
| The Characteristics of Funding in Chinese and American Disciplines and Their Interaction with Scientific Research Output |
| Tian Wencan1,2,Wang Xianwen1,2,Cai Ruonan1,2,Hu Zhigang1,2 |
| (1.Institute of Science of Science and Science and Technology Management, Dalian University of Technology;2.WISE Laboratory, Dalian University of Technology, Dalian 116024, China) |
|
|
|
|
Abstract Research funding has become an essential public finance spending for scientific development. It provides researchers with important financial support for equipment purchase, material needs and personnel training, and plays a vital role in promoting the emerging of frontier fields and enhancing a country′s scientific research competitiveness. However, due to the complexity of data processing, previous research lacked a full-disciplinary perspective to present the research funding situation in China. It was also insufficient by only using descriptive statistics and correlation analysis without in-depth research on the causal relationship or two-way interactive effect between research funding and scientific research output.#br#In this paper,the PVAR model is used to establish a dynamic analysis model based on the data of 5.45 million SCI papers published by both Chinese and American researchers in the past ten years, to explore the interactive effect between research funding and scientific research output from the perspective of endogenous dynamics. There are four questions in this research. (1) What the rates of research fundings in different disciplines in China and the USA? (2) Did the research funding of the two countries promote scientific research output? (3) Did the research fundings have the same effect to scientific research outputs in China and the USA? (4) Did the research funding rates have the same effects on scientific research output in different disciplines?#br#The research results show that the funding rate in China and the USA had maintained a steady growth trend, with an increase of 13 percentage points in the past ten years. Due to differences in resource endowments and development stages, China and the USA had great differences in scientific funding. China was concerned with earth sciences, education and agriculture, while the USA was more concerned with biomedicine, energy and materials. Regarding the interaction effect between the funding rate and scientific research output, the funding rate was the Granger cause of scientific research output, and there was only a one-way Granger causality between the two. However, the relationship between funding rate and scientific research output was not consistent in China and the USA. Research funding promote scientific research output in the USA while inhibited scientific research output in China. As for the specific discipline, the funding rate in engineering fields with a significant increase over the past decade had a significant positive effect on scientific research output. However, due to the relatively small funding in the field of medicine and pharmacy in China, its expected effect on the scientific research output cannot be observed. In the USA, the funding rate had significantly boosted research output primarily in computer science.#br#The research results can provide data support and policy inspiration for China to formulate scientific and reasonable disciplinary development plans, accelerate its construction into a technologically innovative country, and seize the first-mover advantage in the future international competition between China and the USA. By exploring interactive effect between the research funding and scientific research output in various disciplines, this study elaborates the situation and challenges of China's science and technology funding. This not only points out the direction for the development of various disciplines in China in the future, but also provides references for the formulation of China's fund policy by quantitative analysis with accurate data, which is of great significance for China to accelerate the construction of an innovative country. #br#Yet, due to data limitations, we could only measure the output of scientific research by the number of papers, ignoring the investigation of dimensions such as patents. In the follow-up, the output of papers and the number of patent applications can be combined to further study the dynamic interaction between the funding rate and scientific research output.#br#
|
|
Received: 08 November 2021
|
|
|
|
|
|
| [1] 龚旭. 政府与科学——说不尽的布什报告[J]. 科学与社会, 2015, 5(4): 82-101.[2] LANE J. Assessing the impact of science funding[J]. Science, 2009, 324(5932): 1273-1275.[3] 孟婧, 邱长波, 于宝君. 国家自然科学基金对科技论文资助效果研究综述[J]. 情报科学, 2019, 37(12): 166-171.[4] 王菲菲, 贾晨冉, 刘俊婉. 科技项目资助对论文产出绩效的作用效率测度研究——以人工智能领域为例[J]. 科技进步与对策, 2019, 36(16): 26-33.[5] 许鑫, 杨佳颖, 李丹. 科学基金资助下的国际科研合作网络演进:基于WOS物理学数据的计量分析[J]. 情报杂志, 2019, 38(2): 49-55，48.[6] 李昕洁. 基金论文的使用次数是否高于非基金论文[J]. 图书馆论坛, 2019, 39（2): 11-17.[7] 张诗乐, 盖双双, 刘雪立. 国家自然科学基金资助的效果——基于论文产出的文献计量学评价[J]. 科学学研究, 2015, 33(4): 507-515.[8] ALVAREZ-BORNSTEIN B, BORDONS M. Is funding related to higher research impact? exploring its relationship and the mediating role of collaboration in several disciplines[J]. Journal of Informetrics, 2021, 15(1)：101-102.[9] 禾雪瑶, 马廷灿, 岳名亮, 等. 国家自然科学基金资助论文的高被引率变化研究[J]. 数据分析与知识发现, 2021, 5（2): 61-69.[10] 李若筠, 吴登生, 徐伟宣, 等. 中国管理科学学科发展态势计量分析[J]. 中国管理科学, 2020, 28(3): 223-230.[11] 刘强, 董国轩, 常翔. 2019年度国家自然科学基金天文学科常规项目申请和资助情况分析[J]. 天文学进展, 2019, 37(3): 359-365.[12] 张灿影, 鲁景亮, 王琳, 等. 中美自然科学基金海洋科学资助效果与SCIE论文产出比较研究[J]. 世界科技研究与发展,2021,43（6）:677-690.[13] 陈柏彤, 赵飞, 刘龙江. 国家科学基金视角下图书情报与档案管理学科发展分析[J]. 图书情报知识, 2020，38(2):113-122.[14] 党亚茹. 基于SSCI的中国社会科学论文基金资助分析[J]. 重庆大学学报(社会科学版), 2010, 16(5): 55-61.[15] 田文灿, 胡志刚, 焦健, 等. 国家自然科学基金纳米生物医学领域资助项目绩效分析[J]. 中国科学基金, 2019, 33(1): 62-70.[16] 王辉, 刘佳, 钟永恒, 等. 国家自然科学基金资助现状与效果分析——基于国家重点实验室的视角[J]. 科技管理研究, 2020, 40(18): 29-36.[17] 田人合, 郑军卫, 张志强, 等. 科技人力资本视角下杰青科学家资助年龄对资助效果的影响——以杰青基金地球科学项目为例[J]. 图书情报工作, 2019, 63(22): 91-105.[18] XU X, TAN A M, ZHAO S X. Funding ratios in social science: the perspective of countries/territories level and comparison with natural sciences[J]. Scientometrics, 2015, 104(3): 673-684.[19] VANDERELST D, SPEYBROECK N. Scientometrics reveals funding priorities in medical research policy[J]. Journal of Informetrics, 2013, 7(1): 240-247.[20] ALVAREZ-BORNSTEIN B, DIAZ-FAES A A, BORDONS M. What characterises funded biomedical research? evidence from a basic and a clinical domain[J]. Scientometrics, 2019, 119(2): 805-825.[21] WANG X, LIU D, DING K, et al. Science funding and research output: a study on 10 countries[J]. Scientometrics, 2012, 91(2): 591-599.[22] 陈秋怡, 刘海波. 科研基金资助投入与高水平国际论文产出研究——基于六国SCI论文的实证分析[J]. 中国科技论坛, 2018，34(1): 158-163.[23] ZHAO S X, YU S, TAN A M, et al. Global pattern of science funding in economics[J]. Scientometrics, 2016, 109(1): 463-479.[24] ZHANG L, ZHAO W, LIU J, et al. Do national funding organizations properly address the diseases with the highest burden: observations from China and the UK[J]. Scientometrics, 2020, 125(2): 1733-1761.[25] 阅读者的力量——2020年度信息资源与数据管理阅读书单[J]. 信息资源管理学报, 2021, 11(1): 123-135.[26] FORTUNATO S, BERGSTROM C T, BRNER K, et al. Science of science[J]. Science, 2018, 359(6379)：1007.[27] 田人合, 张志强, 高志. 基于分段线性模型的科研项目论文产出评价研究——以杰青基金地球科学项目为例[J]. 科技进步与对策, 2019, 36(1): 142-151.[28] FUYUNO I, CYRANOSKI D. Cash for papers: putting a premium on publication[J]. Nature, 2006, 441(7095): 792-793.[30] JACOB B A, LEFGREN L. The impact of NIH postdoctoral training grants on scientific productivity[J]. Research Policy, 2011, 40(6): 864-874.[30] HOLTZ-EAKIN D, NEWEY W, ROSEN H S. Estimating Vector Autoregressions with Panel Data[J]. Econometrica, 1988, 56(6): 1371-1395.[31] 孙正, 张志超. 流转税改革是否优化了国民收入分配格局——基于“营改增”视角的PVAR模型分析[J]. 数量经济技术经济研究, 2015, 32(7): 74-89.[32] 苏屹, 李丹. 研发投入、创新绩效与经济增长——基于省级面板数据的PVAR实证研究[J]. 系统管理学报, 2021，29(4): 763-770.[33] 徐海峰, 王晓东. 现代服务业是否有助于推动城镇化——基于产城融合视角的PVAR模型分析[J]. 中国管理科学, 2020, 28(4): 195-206.[34] GRANGER C W J. Investigating causal relations by econometric models and Cross-spectral Methods[J]. Econometrica, 1969, 37(3): 424-438.[35] 张璐杰, 张小秋. 国外科研资助机构同行评议质量控制机制及启示[J]. 科技进步与对策, 2017, 34(11): 114-117.[36] 孙玉涛, 张艺蕾. 海外人才引进计划提升了我国大学科研产出吗——以“211”工程大学化学学科为例[J]. 科研管理, 2021, 42(10): 20-27.[37] 黄群慧, 贺俊. 中国制造业的核心能力、功能定位与发展战略——兼评《中国制造2025》[J]. 中国工业经济, 2015,33(6): 5-17. |
|
|
|
