【作者】 徐妍；

【导师】 王燕；

【作者基本信息】 南开大学 ， 产业经济学， 2013， 博士

【摘要】 知识经济时代,大力发展高技术产业是中国抢占新一轮国际竞争制高点的战略性选择。然而,国内各地区的高技术产业发展绩效,特别是技术创新绩效存在明显差距,仍旧延续了“东高西低”的传统区域格局。随着高技术产业对国民经济增长的带动作用日益凸显,其技术创新效率的空间分异会进一步加剧业已扩大的区域经济发展差距。因此,研究高技术产业技术创新效率的影响因素及其空间分异机理对加快经济增长方式转变以及构建社会主义和谐社会都具有一定的政策含义。理论和实践都表明,空间集聚是高技术产业的显著区位特征,与技术创新具有内在关联性。于是,在考察中国高技术产业技术创新效率的影响因素及其空间分异时,产业集聚是一个无法回避的前提和切入点。本文正立足于此,借鉴空间经济学和创新经济学的相关理论,构建了高技术产业集聚创新分析框架,以此揭示从产业集聚到产业技术创新效率空间分异的实现机理,在此基础上,运用随机前沿分析、主成分分析、传统增长收敛性判别以及空间统计分析等方法实证检验了产业集聚对中国高技术产业技术创新效率及其空间分异的影响。在中国经济转型和对外开放背景下,高技术产业集聚动因既包括具有创新创业精神的企业家、缄默知识溢出本地化、产业垂直关联效应、区域创新资源优势、产业成本节约效应等一般因素,也包括FDI和政府作用等符合中国现实的特殊因素。通过产业集聚自组织机制,这些驱动因素又进一步衍生出广义资本积累、知识本地溢出和创新环境优化三种集聚效应,从而对高技术产业技术创新效率及其空间分异产生影响。为检验这一理论框架的适用性,本文基于1997-2011年中国28个省域(不包含西藏、青海和新疆以及港澳台地区)的面板数据,实证分析了高技术产业集聚对其区域技术创新效率的提升、分异作用,结论显示,其一,物质资本与企业家资本的交互作用对创新效率提升具有“1+1>2”的影响效果,但物质资本与劳动型人力资本只有相互匹配才能显著提升创新效率；其二,外资R&D、国外技术引进、大学R&D以及技术消化吸收能力等知识溢出因素都有利于提升创新效率,但以国内技术购买为渠道的知识溢出却发挥反向作用,技术消化吸收能力薄弱使其对各渠道知识溢出的促进作用未充分显现；其三,技术市场化和国内外市场的新产品消费潜力、本地同业竞争以及生产性服务业发展都有利于提升创新效率,但政府支持的促进作用不显著；其四,创新环境优化效应和各集聚效应的交互作用对全国各省域创新效率差异的贡献最大,知识本地溢出效应和创新环境优化效应对东部各省域创新效率差异的贡献最大,各集聚效应的交互作用对中部各省域创新效率差异的贡献最大,知识本地溢出效应和各集聚效应的交互作用对西部各省域创新效率差异的贡献最大；其五,东、中、西部地区的组间效率差距以及东部地区的组内效率差距是全国总体效率差距的主要构成,仅全国和中部地区存在省域创新效率的绝对β收敛,但全国和东、中、西部地区都存在省域创新效率的条件β收敛,就全国而言,各省域创新效率呈现“高-高”邻近、“低-低”邻近的空间正关联模式,印证了中国高技术产业技术创新效率存在“东高西低”两大俱乐部收敛。本文的可能创新点体现为：其一,现有文献在选取高技术产业技术创新效率的影响因素时缺乏一定的系统性,本文则尝试将产业集聚引入分析框架,通过梳理高技术产业集聚动因归纳出广义资本积累、知识本地溢出和创新环境优化三条影响路径,从而使分析结论更具系统性；其二,现有文献在处理产业集聚效应的时间滞后性时通常采用滞后一期的做法,本文则借助面板Granger因果检验将相关变量设为滞后四期,从而使分析结论更具客观性；其三,现有文献在考察区域高技术产业技术创新效率差异变动趋势时大都采用变异系数等简易指标,本文则综合运用传统增长收敛性判别、空间相关性判别等方法进行考察,从而使分析结论更具稳健性。更多还原

【Abstract】 In the era of Knowledge Economy, to vigorously develop high-tech industry is a strategic choice for China to seize the commanding heights of a new round of international competition. However, there exist significant gaps among China’s provinces as regards technology innovation efficiency of high-tech industry. It’s a continuation of China’s long-term region pattern that Eastern China is far ahead of Western China in development performance. As the development of high-tech industry becomes an increasingly powerful booster of economic growth, the spatial dissimilarity of its technology innovation efficiency will worsen the widening provincial gaps. Therefore, to study the impact factors and the spatial dissimilarity mechanism of technology innovation efficiency of high-tech industry, is helpful to get some policy implications about accelerating the transformation of economic growth and the construction of a socialist harmonious society. Both theory and practice suggest that spatial agglomeration is an important location characteristic of high-tech industry, and has an intrinsic relationship with technology innovation. Thus, industrial agglomeration is an unavoidable premise and starting point for the investigation of the impact factors and the spatial dissimilarity mechanism of technological innovation efficiency of China’s high-tech industry. Based on the above thinking and the related theories of spatial economics and innovation economics, this paper has tried to constructed an agglomeration-innovation model of high-tech industry, for the purpose of revealing the influence mechanism of industrial agglomeration on technology innovation efficiency, then has empirically examined it by some analysis methods.In the background of China’s economic trans formation and opening to the outside world, the agglomeration motives of high-tech industry include entrepreneurs with the spirit of innovation and entrepreneurship, localized tacit-knowledge spillover, vertical connection effect, innovation resources advantage, cost-saving effect, foreign direct investment and government’s role. Through the self-organization mechanism of industrial agglomeration, these motives further develop three effects, that is, generalized capital accumulation, knowledge local spillovers and innovation environment optimization, through which industrial agglomeration influences technology innovation efficiency and its spatial dissimilarity. As to the applicability of the above theoretical framework, this paper has provided empirical evidence from the provincial panel data during1997-2011, not including Tibet, Qinghai, Xinjiang, Hong Kong, Macao and Taiwan. The conclusions show, firstly, the interaction of physical capital and entrepreneur human capital has a "1+1>2" effect on innovation efficiency improvement, but only does physical capital and labor human capital match each other, innovation efficiency can be significantly improved; secondly, knowledge-spillover factors, such as foreign R&D, foreign technology acquisition, university R&D and technology assimilation are all conductive to enhance innovation efficiency, but domestic technology acquisition plays a reverse role, weakness in technology assimilation has led to its inadequate role in promoting innovation efficiency; thirdly, technology commercialization and consumption potential for new products of domestic and foreign markets, local horizontal competition and the development of productive service industry are all beneficial to upgrade innovation efficiency, but the role of government’s supports isn’t significant; fourthly, the innovation environment optimization effect and the interaction of the three agglomeration effects make the greatest contribution to the variance in innovation efficiency nationwide, the knowledge local spillovers effect and the innovation environment optimization effect are two main contributors to the variance in innovation efficiency in eastern China, the interaction of the three agglomeration effects is the biggest contributor to the variance in innovation efficiency in central China, the knowledge local spillovers effect and the interaction of the three agglomeration effects make the greatest contribution to the variance in innovation efficiency in western China; last but not the least, the innovation efficiency gaps between eastern, central and western parts of China and the innovation efficiency gaps within eastern China are the main composition of the variance nationwide, the three parts all have conditional β convergence, there exists a positive spatial association pattern that provinces with high innovation efficiency adjacent to each other, while provinces with low innovation efficiency adjacent to each other, confirming the existence of a two-club convergence of provincial innovation efficiency nationwide.In the normative analysis, existing literature lacks systematization while selecting the impact factors on technology innovation efficiency of high-tech industry, but this paper introduced industrial agglomeration into the analysis framework, and inducted three influence mechanisms, that is, generalized capital accumulation, knowledge local spillovers and innovation environment optimization derived from the agglomeration motives of high-tech industry, thus to make conclusions more systematic. In the empirical analysis, firstly, existing literature often lags one period while processing time-lag problem, but this paper is to lag four periods based on the Granger causality test, thus to make conclusions more objective; secondly, existing literature often uses coefficient of variation to investigate the trend of spatial dissimilarity of technology innovation efficiency of China’s high-tech industry, but this paper comprehensively used traditional growth convergence criteria and spatial correlation criteria, thus to make conclusions more robust.更多还原