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| The Partial DEA Window Analysis and the Efficiency Spatial Correlation Under the Capital Effect of China′s Scientific and Technological Input Over Efficiency Measure |
| Guo Lu1,Dai Zhimin2 |
| (1.School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China;2.School of Economics&Management,Nanchang University,Nanchang 330025,China) |
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Abstract The existing researches on the efficiency of scientific and technological activities have serious heterogeneity defects, so there is the need for an in-depth analysis of the regional efficiency difference. This study uses the super-efficiency measure theory and DEA window method to empirically analyze the dynamic evolution of science and technology activities in 30 provinces (cities) in China from 2010 to 2019. It constructs a panel data equation from the perspective of investment in science and technology to examine the driving force of different capital types, and the direct and indirect effects of space overflow in China's input in science and technology efficiency to confirm if the capital difference plays a leading role in the annual average efficiency of China's scientific and technological activities. Moran's index is used to measure the correlation types of efficiency and spatial agglomeration of science and technology activities in different provinces (cities). The research shows that in the past ten years, the efficiency of science and technology activities in China has shown an initial rapid rise,but the trend markedly decreased in recent years. Among them, the efficiency of Northeast China is saturated and the capital investment is redundant. High efficiency provinces (cities) have outstanding driving ability. The efficiencies in central and southern China have spirally increased, and there are obvious differences among the groups in each province (city) in the region. The efficiency in northwestern region and southwestern region is high, but there are obvious differences in provincial (city) differentiation. As a whole, the efficiency of technology activities in China overflows obviously. #br#It is found that government funds and enterprise funds in North China, Central South and southwest have a significant negative effect on the efficiency of science and technology investment in this region, and a significant positive spillover effect; and government funds and enterprise funds have a positive total effect on the efficiency of science and technology investment in northeast China government funds and enterprise funds have a significant negative effect on the efficiency of science and technology investment in this region. But the significant positive spatial spillover effects on adjacent regions are different, indicating that the effect of government capital investment in Northeast China is close to saturation, and there is still a large space for enterprise capital investment. Government and enterprise funds in East China have different significant negative effects on the efficiency of science and technology investment in this region, and also have different positive spatial spillover effects on adjacent regions, indicating that the government capital investment in East China is close to saturation, and there is much room for the transformation of enterprise capital investment. The degree of convergence of the spatial correlation between the efficiency transfer of science and technology investment and spatial agglomeration in 30 provinces (cities) is spiraling, the degree of spatial correlation between provinces is obviously close, and the efficiency of science and technology investment in most provinces (cities) is still in the "capital pursuit". In the benefit acquisition stage, the spatial spillover effect is not obvious, and has not yet transitioned to the capital driven value discovery stage. #br#It is suggested that departments in charge of scientific and technological activities at all levels, enterprises and institutions should concentrate limited resources on scientific and technological projects with more basic and important breakthrough connotation. Secondly it is essential to fully understand the effects of different capital sources in the region to promote the efficiency of investment in scientific and technological activities. Relevant provinces (cities) in the region should carefully clarify the mutual influences and effect deduction degrees of different capital items including government investment, enterprise investment and social investment. It’s vital to pay attention to the dual difference of "quantity and quality" in the continued use of government funds; in many regions, there is still a large amount of space for enterprise capital investment. Thirdly in terms of the total amount, it is still necessary to increase the overall investment in various scientific and technological resources and earnestly fulfill the inevitable requirements of the transformation of service-oriented government. It is critical to clearly understand the mainstream scientific and technological development trend, and make reasonable resource distribution patterns of scientific and technological investment so as to track the forefront of science and technology.#br#
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Received: 02 April 2021
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| [1] 黄群慧.论中国工业的供给侧结构性改革[J].中国工业经济,2016，23(9):5-23.[2] 江静,刘志彪,于明超.生产者服务业发展与制造业效率提升:基于地区和行业面板数据的经验分析[J].世界经济,2007，30(8):52-62.[3] 张占斌.中国经济新常态的趋势性特征及政策取向[J].国家行政学院学报,2015(1):15-20.[4] LENZEN M.A generalized input-output multiplier calculus for Australia[J].Economic systems Research,2001,13(1):65-92[5] GRILICHES Z.Issues in assessing the contribution of R&D to productivity growth [J].Bell Journal of Economics, 1979, 10(1):92-116.[6] LICHTENBERG F.R&D investment and international productivity difference[R]. National Bureau of Economic Research, 1992.[7] DOMINIQUE GUELLEC.R&D and productivity growth: panel aata analysis of 16 OECD countries[R].OECD Working Papers,2001.[8] PELIN DEMIREL,MARIANA MAZZUCATO.Does market selection reward innovators[R].FINNOV,2010.[9] WINTER S G. Understanding dynamic capabilities[J]. Strategic Management Journal, 2003, 24(10): 991-995.[10] MARIO COCCIA,SECONDO ROLFO.Technology transfer analysis in the Italian national research council[J].Technovation, 2002, 22(5):291-299.[11] DOMINIQUE GUELLEC,BRUNO.From R&D to productivity growth: do the institutional settings and the source of funds of R&D matter[J]. Oxford Bulletin Of Economics and Statistics, 2004，66（3）：353-378.[12] LEV B,SOUGIANNIS T.The capitalization, amortization and value relevance of R&D[J].Journal of Accounting and Economics,1996,21(1):107-138.[13] INONU E.The influence of cultural factors on scientific production [J].Scientometrics, 2003, 56(1):137-146.[14] ROUSSEAU S,ROUSSEAU R.Data envelopment analysis as a tool for constructing scientometric [J].Scientometrics,1997,40(1):45-46.[15] 池仁勇,黄英杰.浙江省R&D活动非效率成因分析[J].科技进步与对策,2009,26(17):41-44.[16] 李伟红,胡宝民.基于Malmquist方法的我国区域R&D投入效率比较研究[J].工业技术经济,2011,30(8):33-38.[17] 李惠娟,赵静敏,上官敬芝.城市科技资源配置效率的DEA两阶段方法评价——基于江苏省十三个城市的研究[J].科技管理研究,2010,30(19):57-60.[18] 朱慧,傅为忠,刘彦华.安徽省科技资源配置效率及其影响因素研究[J].合肥工业大学学报(社会科学版),2013,27(4):13-19.[19] 岳书敬.外商直接投资与创新能力-基于中国高技术产业的实证分析[J].科技管理研究,2009,29(8):412-414.[20] 史欣向,梁彤缨.社会资本影响了研发效率——基于中国省际面板数据的经验研究[J].科研管理,2013,34(5):73-79，110.[21] 卫平,赵良浩.我国战略性新兴产业科技成果转化效率研究[J].工业技术经济,2014,33(1):11-20.[22] 范秋芳,杨敏,崔珊.知识产权保护背景下中国省域科技创新效率研究[J].中国石油大学学报(社会科学版),2017,33(4):15-20.[23] 夏业良,程磊.外商直接投资对中国工业企业技术效率的溢出效应研究:基于2002-2006年中国工业企业数据的实证分析[J].中国工业经济,2010(7):55-65.[24] 付空.我国财政科技支出效率评价及影响因素研究[D].重庆：西南大学,2016.[25] 韩凤芹,赵伟.R＆D中政府投入效率测度及影响因素分析-基于省级面板数据的研究[J].财政研究,2015 (11):75-85.[26] 李尽法.财政科技支出动态效率测度研究[J].统计与决策,2010 (19):102-104.[27] 俞立平,熊德平,武夷山.中国地区科技效率的组合测度研究[J].科学学研究,2011,29(8):1141-1146.[28] 刘绘珍.河南省城市科技效率评价研究[J].郑州航空工业管理学院学报,2014,32(3):34-40.[29] 颜伟.基于两阶段DEA模型的煤炭行业上市公司科技效率研究[J].中国煤炭,2015,41(11):11-15.[30] 睢党臣,董玉迪.中国新丝绸之路经济带科技效率的测度及分解——基于DEA-Malmquist指数的方法[J].地域研究与开发,2015,34(5):19-23.[31] 余泳泽.创新要素集聚、政府支持与科技创新效率——基于省域数据的空间面板计量分析[J].经济评论,2011(2):93-101.[32] COOPER W W,THRALL R M.Introduction:extensions and new developments in DEA[J].Annuals of operations Research，1996, 66(1): 1-45.[33] BANKER R D,CHARNES A,COOPER W W.Some models for estimating technical and scale inefficiencies in data envelopment analysis[J].Management Science,1984, 30(9): 1078-1092.[34] MUNSHI NASER IBNE AFZAL, ROGER LAWREY. A measurement framework for knowledge-based economy (KBE) efficiency in ASEAN:a data envelopment(DEA)window approach[J].International Journal of Business and Management,2012,7(18):57-68.[35] 曹勇,佘硕.基于动态分析的中国知识密集型服务业概念与分类研究[J].管理学报,2009,6(4):554-559.[36] 邓悦.科技服务业发展水平与区域研发效率关系研究——基于省级行政区域的实证研究[D].西安：西安电子科技大学，2014.[37] 洪银兴.科技金融及其培育[J].经济学家,2011 (6):22-28.[38] 程华,王恩普.科技活动中外部筹资对研发产出的影响——基于高技术产业面板数据的实证分析[J].科学学与科学技术管理,2009 (11):49-56.[39] 郭研,刘一博.高新技术企业研发投入与研发绩效的实证分析-来自中关村的证据[J].经济科学,2011 (2):117-128.[40] 李平,崔喜君,刘建.中国自主创新中研发资本投入产出绩效分析-兼论人力资本和知识产权保护的影响[J].中国社会科学,2007(2):32-42，204-205.[41] 荣岩.FDI的资本效应与溢出效应之比较-基于中国数据的协整分析[J].对外经济贸易大学学报,2010 (6):58-65.[42] L G GODFREYAM R VEALLB. Bootstrap-based critical values for tests of common factor restrictions[J]. Economics Letters,1998,59(1):1-5. |
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