高端装备制造业技术创新资源配置效率及影响因素研究——基于两阶段StoNED和Tobit模型的实证分析

doi:10.16381/j.cnki.issn1003-207x.2018.01.002

摘要/Abstract

摘要： 新常态下，高端装备制造业在推动国民经济稳定增长、加快传统工业转型升级、提升产业链核心竞争力的过程中，先导性和战略性作用日益凸出，但其技术创新资源配置的合理性和有效性却成为产业持续发展的主要瓶颈。本文从投入产出角度出发，基于价值链理论，将技术创新活动分为技术研发和技术转化两个阶段，并构建两阶段StoNED模型，采用2011-2014年的行业面板数据作为样本数据，对高端装备制造业技术创新资源配置效率进行测度和比较；以产业组织为视角，利用Tobit模型分析影响效率的关键因素。实证研究表明，我国高端装备制造业技术创新资源配置的整体和阶段效率在子行业间存在不同程度的波动和差异，技术研发阶段效率偏低制约着整体效率的最优化；甄别影响因素发现，企业规模对技术研发阶段效率具有负向作用，对整体和技术转化阶段效率具有正向作用；市场垄断不利于整体和技术转化阶段效率的提升；政府支持对整体和两阶段效率均具有负面影响。在此基础上，进一步提出应对产业技术创新资源配置现状的对策建议。

关键词: 高端装备制造业, 技术创新资源配置效率, 两阶段StoNED模型, Tobit模型, 影响因素

Abstract: Under the new normal, high-end equipment manufacturing industries are becoming more and more important in promoting the steady growth of the national economy, accelerating the transformation and updating of the traditional industries, and enhancing the core competence of the industry chain. However, the rationality and validity of the allocation of the technological innovation resource has become the main bottle-neck restrictions of the sustainable development of the industries. Based on the evalue chain theory, the technological innovation activities are divided into two stages from the perspective of input-output, including technology research and development and technology transformation. Then, a two-stage stoned model is constructed to measure and compare the technological innovation resource allocation efficiencg of high-end equipment manufacturing and industries by using the industry panel data covering the sample period of 2011-2014. Meanwhile, the tobit model is used to analyzing the key factors affecting the efficiency via the perspective of the industrial organization. The empirical results show that the total efficiencg and stage efficiency differ in degree and volatility among these sub-industries and the efficiency of technological research and development stage is so low that it limits the optimization of the total efficiency level. By identifying the above factors, it turns out that the enterprise's size has negative effect on the efficiency of the technology transformation stage. The government support has remarkably negative influence on both the total efficiency and two stage efficiencies. Based on the above analysis results, this paper puts forward some policy suggestions on dealing with present situation of the allocation of the technological innovation resources are provided.

Key words: high-end equipment manufacturing industries, technological innovation resource allocation efficiency, two-stage StoNED model, tobit model, influencing factors

中图分类号:

范德成, 杜明月. 高端装备制造业技术创新资源配置效率及影响因素研究——基于两阶段StoNED和Tobit模型的实证分析[J]. 中国管理科学, 2018, 26(1): 13-24.

FAN De-cheng, DU Ming-yue. Research on Technological Innovation Resource Allocation Efficiency and Its Influencing Factors in High-end Equipment Manufacturing Industries——Based on the Empirical Analysis of the Two-stage StoNED-Tobit Model[J]. Chinese Journal of Management Science, 2018, 26(1): 13-24.

参考文献

[1] 黄群慧,李晓华.中国工业发展"十二五"评估及"十三五"战略[J].中国工业经济,2015,(9):5-20.

[2] 吕岩威,孙慧.中国战略性新兴产业技术效率及其影响因素研究——基于18个大类行业面板数据的分析[J].科学学与科学技术管理,2013,(11):137-146.

[3] Farrell M J.The measurement of productive efficiency[J].Journal of Royal Statistical Society,1957,20(3):253-281.

[4] Afriat S N.Efficiency estimation of production functions[J].International Economic Review,1972,13(3):568-598.

[5] Rabb R A,Kotamraju P.The efficiency of the high-tech economy:Conventional development indexes versus a performance index[J].Journal of Regional Science,2006,46(3):545-562.

[6] Nelson R.National systems of innovation:A Comparative Analysis[M].Oxford:Oxford University Press,1993.

[7] Porter M.Cluster and the new economic of competition[J].Harvard Business Review,1998,76(6):77-90.

[8] Schmidt R L.Stochastic optimization model to improve production planning and R&D resource allocation in biopharmaceutical production processes[J].Management Science, 1996,42(4):603-617.

[9] Liu C C,Chen C Y.A two-dimensional model for allocating resources to R&D programs[J].Journal of American Academy of Business,2004,5(1):459-473.

[10] 官建成,陈凯华.我国高技术产业技术创新效率的测度[J].数量经济技术经济研究,2009,(10):19-33.

[11] 牛泽东,张倩肖.中国装备制造业的技术创新效率[J].数量经济技术经济研究,2012,(11):51-67.

[12] 何海燕,林波,张剑.技术壁垒对我国企业科技资源配置的影响[J].科学学研究,2011,(6):856-860+839.

[13] 范斐,杜德斌,李恒.区域科技资源配置效率及比较优势分析[J].科学学研究,2012,(8):1198-1205.

[14] 汪朗峰,伏玉林.高技术产业发展中科技资源配置研究[J].科研管理,2013,(2):152-160.

[15] 黄海霞,张治河.基于DEA模型的我国战略性新兴产业科技资源配置效率研究[J].中国软科学,2015,(1):150-159.

[16] Scherer F M,Ross D.Industrial market structure and economic performance[M].Boston:Houghton Mifflin,1990.

[17] Chen C T,Chien C F,Lin M H,et al.Using DEA to evaluate R&D performance of the computers and peripherals firms in Taiwan[J].International Journal of Business, 2004,9(4):347-359.

[18] Zhang Anming, Zhang Yimin,Zhao R.A study of the R&D efficiency and production of Chinese firms[J].Journal of Comparative Economic,2003,31(3):443-464.

[19] 冯根福,刘军虎,徐志霖.中国工业部门研发效率及其影响因素实证分析[J].中国工业经济,2006,(11):46-51.

[20] 陈修德,梁彤缨.中国高新技术产业研发效率及其影响因素——基于面板数据SFPF模型的实证研究[J].科学学研究,2010,(8):1198-1205.

[21] 朱有为,徐康宁.中国高技术产业研发效率的实证研究[J].中国工业经济,2006,(11):38-45.

[22] 白俊红,江可申,李婧.应用随机前沿模型评测中国区域研发创新效率[J].管理世界,2009,(10):51-61.

[23] 籍艳丽,赵丽琴.一种效率测度的新方法:随机非参数数据包络分析法[J].统计与决策,2011,(5):33-34.

[24] Kuosmanen T.Stochastic nonparametric envelopment of data:Combining virtue of SFA and DEA in a unified framework[R].Discussion Paper,MTT Agrifood Research Finland,2006.

[25] Kuosmanen T.Stochastic nonparametric envelopment of panel data:Frontier estimation with fixed and random effects approaches[R].Discussion Paper,MTT Agriffood Research Finland,2007.

[26] 周四军,王欣,胡瑞.中国商业银行X效率测度——StoNED方法与超效率DEA方法的比较研究[J].统计与信息论坛,2012,(4):3-9.

[27] 袁丽静,杨琼.基于StoNED模型的技术效率半参数分析——以钢铁行业为例[J].财经问题研究,2015,(5):35-41.

[28] Cornwell C,Schmidt P,and Sickles R.Production frontiers with cross sectional and time series variation in efficiency levels[J].Journal of Econnometrics 1990,46(1-2):185-200.

[29] 汪茂泰,何永芳.动态随机非参数数据包络分析法及其应用[J].统计与决策,2015,(21):83-85.

[30] 郑坚.高技术产业技术创新效率评价的改进DEA方法研究[D].哈尔滨;哈尔滨工业大学,2008.

[31] 肖仁桥,钱丽,陈忠卫.中国高技术产业创新效率及其影响因素研究[J].管理科学,2012,25(5):85-98.

[32] 王亮.区域创新系统资源配置效率的演进规律与创新机制研究[D].长春:吉林大学,2008.

[33] Sharma S,Thomas V J.Inter-country R&D efficiency analysis:An application of data envelopment analysis[J].Scientometrics,2008,76(3):483-501.

[34] Guan Jiancheng,Gao Xia.Exploring the H-index at patent level[J].Journal of the American Society for Information Science and Technology,2009, 60(1):35-40.

[35] 刘伟,陈菁泉,李星星.中国省际高新技术产业技术创新的TFP测算及收敛趋势研究[J].经济理论与经济管理,2013,33(1):36-50.

[36] Griliches,Z.R&D and the productivity slowdown[J].American Economic Review,1980,70(2):343-348.

[37] Goto A,Suzuki K.R&D capital,rate of return on R&D investment and spillover of R&D in Japanese manufacturing industries[J].The Review of Economics and Statistics,1989,71(4):555-564.

[38] 刘伟.基于Bootstrap-Malmquist指数的高新技术产业技术创新效率分析[J].经济学动态,2013,(3):42-52.

[39] 张军,吴桂英,张吉鹏.中国省际物质资本存量估算:1952-2000[J].经济研究,2004,(10):35-44.

[40] 张娜,杨秀云,李小光.我国高技术产业技术创新影响因素分析[J].经济问题探索,2015,(1):30-35.

[41] 肖文,林高榜.政府支持、研发管理与技术创新效率——基于中国工业行业的实证分析[J].管理世界,2014,(4):71-80.

[42] 王美强,李勇军.具有双重角色和非期望要素的供应商评价两阶段DEA模型[J].中国管理科学,2016,24(12):91-97.