长江经济带创新环境对科技创新效率影响的实证研究
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摘要
基于2006—2015年长江经济带11个省份中108个市级面板数据,运用空间面板计量的固定效应与随机效应模型实证检验了创新环境对区域创新效率的影响,并且使用空间面板自回归二阶段最小二乘法进行了稳健性检验。实证研究发现:长江经济带上游和中游地区基础设施环境对创新效率的影响要显著高于长江下游地区;经济环境对长江上游区域的创新效率的促进作用很显著,但对长江中下游区域并不显著;间接融资金融环境水平提升并不能有效促进地区创新效率的提高;人文教育环境与财政环境对地区创新效率的影响不显著。需要从改善创新环境等方面着手,提升地区科技创新效率。
Based on the panel data of 108 cities in 11 provinces along the Yangtze River Economic Belt in 2006-2015, this paper conducts an empirical test of the impact of innovation environment on the regional innovation efficiency by making use of the fixed effect of the special panel metric and the random effect model. Then it conducts a robustness test by employing the space panel autoregressive two-stage least square method. The findings show that the impact of the infrastructure environment at the upper and middle reaches of the Yangtze River Economic Belt on the innovative efficiency is significantly higher than that at the lower reaches, that the promotion effect of economic environment on the innovation efficiency at the upper reaches of the Yangtze River is significant, but not significant at the upper and middle reaches of Yangtze River, that the improvement of the indirect financing environment cannot effectively promote the improvement of regional innovation efficiency, and that the impact of the humanistic education environment and the fiscal environment on regional innovation efficiency is not significant. Thus it is necessary to improve the innovation environment and promote the efficiency of regional technological innovation.
Based on the panel data of 108 cities in 11 provinces along the Yangtze River Economic Belt in 2006-2015, this paper conducts an empirical test of the impact of innovation environment on the regional innovation efficiency by making use of the fixed effect of the special panel metric and the random effect model. Then it conducts a robustness test by employing the space panel autoregressive two-stage least square method. The findings show that the impact of the infrastructure environment at the upper and middle reaches of the Yangtze River Economic Belt on the innovative efficiency is significantly higher than that at the lower reaches, that the promotion effect of economic environment on the innovation efficiency at the upper reaches of the Yangtze River is significant, but not significant at the upper and middle reaches of Yangtze River, that the improvement of the indirect financing environment cannot effectively promote the improvement of regional innovation efficiency, and that the impact of the humanistic education environment and the fiscal environment on regional innovation efficiency is not significant. Thus it is necessary to improve the innovation environment and promote the efficiency of regional technological innovation.
引文
[1]严翔,成长春.长江经济带科技创新效率与生态环境非均衡发展研究———基于双门槛面板模型[J].软科学,2018,(2):11-15.
[2]Jefferson Gary H.,Bai Huamao,Guan Xiaojing,Yu Xiaoyun. R&D Performance in Chinese Industry[J]. Economics of Innovation and New Technology, 2006, 15(4):2-13.
[3]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.
[4]王圣云,林玉娟,罗玉婷.长江经济带科技创新效率变化的指数分解及聚类分析[J].华东经济管理,2018,(9):66-72.
[5]吴传清,黄磊,文传浩.长江经济带技术创新效率及其影响因素研究[J].中国软科学,2017,(5):160-170.
[6]蒋含明.外商直接投资知识溢出、信息化水平与技术创新能力[J].江西财经大学学报,2019,(1):34-42.
[7]叶丹,黄庆华.区域创新环境对高技术产业创新效率的影响研究———基于DEA-Malmquist方法[J].宏观经济研究,2017,(8):132-140.
[8]李习保.区域创新环境对创新活动效率影响的实证研究[J].数量经济技术经济研究,2007,(8):13-24.
[9]杜程鹏,李敏,洪艳.我国中部地区技术创新效率差异性研究[J].科技进步与对策,2014,(4):36-39
[10]周海涛,张振刚.政府科技经费对企业创新决策行为的引导效应研究———基于广东高新技术企业微观面板数据[J].中国软科学,2016,(6):110-120.
[11]刘迎春.中国战略新兴产业技术创新效率实证研究———基于DEA方法的分析[J].宏观经济研究,2016,(6):43-48.
[12]丁焕峰,邱梦圆.技术创新的结构布局、专业化优势与经济增长———基于动态面板模型的实证分析[J].江西财经大学学报,2018,(3):11-22.
[13]吴延兵. R&D存量、知识函数与生产效率[J].经济学(季刊),2006,(3):1129-1156.
[14]戴魁早,刘友金.行业市场化进程与创新绩效:中国高技术产业的经验分析[J].数量经济技术经济研究,2013,(9):37-54.
[15]戴魁早,刘友金.要素市场扭曲与创新效率———对中国高技术产业发展的经验分析[J].经济研究,2016,(7):72-86.
[16]Sivak R., Caplanova A., Hudson J.. The Impact of Governance and Infrastructure on Innovation[J]. Post-Communist Economies, 2011, 32(2):203-217.
[17]Wardyn M. R.. Knowledge Flows, Technological Change and Regional Growth in the European Union[EB/OL]. http://link. springer. com/chapter/10. 1007/978-3-319-00342-9_3, 2013-05-05.
[18]Raab 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.
[19]Sun C., Kalirajan K. P.. Gauging the Sources of Growth of High-Tech and Low-Tech Industries:The Case of Korean Manufacturing[J]. Australian Economic Papers, 2005, 44(2):170-185.
[20]Fisk D.. What Are the Risk-Related Barriers to and Opportunities for:Innovation from a Business Perspective in the UK in the Context of Energy Management in the Built Environment[J]. Energy Policy, 2008, 36(12):4615-4617.
[21]Furman J. L., Porter M. E., Stern S.. The Determinants of National Innovative Capacity[J]. Research Policy, 2002,(31):899-933.
[22]Riddel M., Schwer R. K.. Regional Innovative Capacity Endogenous Employment:Empirical Evidence from the U.S.[J].The Review of Regional Studies, 2003, 33(1):73-84.
[23]彭镇华,廖进球,习明明.资本配置效率、经济增长与空间溢出效应[J].证券市场导报,2018,(2):22-30.
(1)当前学术界测度创新效率的方法主要分为以随机前沿分析法(SFA)为代表的参数法,以数据包络分析法(DEA)为代表的非参数法。随机前沿分析法(SFA)主要是用来评价单一产出多投入技术创新效率问题,所以SFA模型不能充分地反映区域的创新产出。
(1)长江经济带拥有近130个行政单元,但是中西部地区各个少数民族自治州、贵州铜仁市和毕节市与安徽巢湖市存在数据缺失情况,尤其是发明专利数据缺失严重,因此样本中剔除了这20多个行政单元。
(2)佰腾网是国内知识经济领域第一门户,集成了专利信息应用服务、科技成果转化服务、产学研对接服务、国际技术转移服务为一体,拥有专利数据库、专利监控、专利预警、企业贯标等多项知识产权产品服务,覆盖全球103个国家、区域与组织,数据量达1.3亿件。
[2]Jefferson Gary H.,Bai Huamao,Guan Xiaojing,Yu Xiaoyun. R&D Performance in Chinese Industry[J]. Economics of Innovation and New Technology, 2006, 15(4):2-13.
[3]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.
[4]王圣云,林玉娟,罗玉婷.长江经济带科技创新效率变化的指数分解及聚类分析[J].华东经济管理,2018,(9):66-72.
[5]吴传清,黄磊,文传浩.长江经济带技术创新效率及其影响因素研究[J].中国软科学,2017,(5):160-170.
[6]蒋含明.外商直接投资知识溢出、信息化水平与技术创新能力[J].江西财经大学学报,2019,(1):34-42.
[7]叶丹,黄庆华.区域创新环境对高技术产业创新效率的影响研究———基于DEA-Malmquist方法[J].宏观经济研究,2017,(8):132-140.
[8]李习保.区域创新环境对创新活动效率影响的实证研究[J].数量经济技术经济研究,2007,(8):13-24.
[9]杜程鹏,李敏,洪艳.我国中部地区技术创新效率差异性研究[J].科技进步与对策,2014,(4):36-39
[10]周海涛,张振刚.政府科技经费对企业创新决策行为的引导效应研究———基于广东高新技术企业微观面板数据[J].中国软科学,2016,(6):110-120.
[11]刘迎春.中国战略新兴产业技术创新效率实证研究———基于DEA方法的分析[J].宏观经济研究,2016,(6):43-48.
[12]丁焕峰,邱梦圆.技术创新的结构布局、专业化优势与经济增长———基于动态面板模型的实证分析[J].江西财经大学学报,2018,(3):11-22.
[13]吴延兵. R&D存量、知识函数与生产效率[J].经济学(季刊),2006,(3):1129-1156.
[14]戴魁早,刘友金.行业市场化进程与创新绩效:中国高技术产业的经验分析[J].数量经济技术经济研究,2013,(9):37-54.
[15]戴魁早,刘友金.要素市场扭曲与创新效率———对中国高技术产业发展的经验分析[J].经济研究,2016,(7):72-86.
[16]Sivak R., Caplanova A., Hudson J.. The Impact of Governance and Infrastructure on Innovation[J]. Post-Communist Economies, 2011, 32(2):203-217.
[17]Wardyn M. R.. Knowledge Flows, Technological Change and Regional Growth in the European Union[EB/OL]. http://link. springer. com/chapter/10. 1007/978-3-319-00342-9_3, 2013-05-05.
[18]Raab 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.
[19]Sun C., Kalirajan K. P.. Gauging the Sources of Growth of High-Tech and Low-Tech Industries:The Case of Korean Manufacturing[J]. Australian Economic Papers, 2005, 44(2):170-185.
[20]Fisk D.. What Are the Risk-Related Barriers to and Opportunities for:Innovation from a Business Perspective in the UK in the Context of Energy Management in the Built Environment[J]. Energy Policy, 2008, 36(12):4615-4617.
[21]Furman J. L., Porter M. E., Stern S.. The Determinants of National Innovative Capacity[J]. Research Policy, 2002,(31):899-933.
[22]Riddel M., Schwer R. K.. Regional Innovative Capacity Endogenous Employment:Empirical Evidence from the U.S.[J].The Review of Regional Studies, 2003, 33(1):73-84.
[23]彭镇华,廖进球,习明明.资本配置效率、经济增长与空间溢出效应[J].证券市场导报,2018,(2):22-30.
(1)当前学术界测度创新效率的方法主要分为以随机前沿分析法(SFA)为代表的参数法,以数据包络分析法(DEA)为代表的非参数法。随机前沿分析法(SFA)主要是用来评价单一产出多投入技术创新效率问题,所以SFA模型不能充分地反映区域的创新产出。
(1)长江经济带拥有近130个行政单元,但是中西部地区各个少数民族自治州、贵州铜仁市和毕节市与安徽巢湖市存在数据缺失情况,尤其是发明专利数据缺失严重,因此样本中剔除了这20多个行政单元。
(2)佰腾网是国内知识经济领域第一门户,集成了专利信息应用服务、科技成果转化服务、产学研对接服务、国际技术转移服务为一体,拥有专利数据库、专利监控、专利预警、企业贯标等多项知识产权产品服务,覆盖全球103个国家、区域与组织,数据量达1.3亿件。