【作者】 林云；

【导师】 金祥荣；

【作者基本信息】 浙江大学 ， 政治经济学， 2008， 博士

【摘要】 在新增长理论将技术视为内生变量以来,对于技术创新内生性问题的关注与日俱增。在梳理国内外文献的基础上,对研发创新路径进行分析,并着重对中国区域及产业层次的技术创新效率进行经验分析,结论指出,在技术创新投入、产出到推动区域经济发展的过程中,技术创新效率都有着决定性的作用。本文研究既是对增长理论中技术内生性问题的有益补充,也对中国从创新“大”国转变为创新“强”国提供了可行路径。论文得到的主要结论如下:企业的研发路径与企业所处技术知识环境密切相关,在企业追求创新效率最大化的条件下,技术机会及可吸收的技术知识越多,企业的研发路径越趋向于同质化,研发支出的动力也更足。因此,构建有利于技术创新的技术知识环境非常重要。研发、内生吸收能力、技术机会等创新投入因素与创新产出之间不再是简单的线性关系,在技术创新效率的作用下,创新投入与创新产出的正向累积循环形成了区域创新“轨道”,使得创新先发优势继续保持并不断扩大,区域技术创新产出呈现日益明显的“马太效应”。在技术创新体系中,由于研发目的及研发方向的不同,政府、企业、高校、研究机构具有明显不同的创新效率,从我国东部尤其是东部沿海经济区的高创新效率经验来看,企业创新+政府扶持+创新累积的创新模式取得了最优的创新效率。目前,企业在技术创新体系中的创新主体地位已经基本形成,政府大量投资于高校和研究机构,但他们的创新效率并不高,对于专利产出有负向的影响。高校和研究机构在技术创新体系中作用不显著,一个重要的原因在于高校、研究机构与企业的合作,也即产学研合作的效率比较低。应该建立以企业为创新主体,高校、科研机构大力合作的产学研合作体系。政府在技术创新领域的更重要功能在于为企业创造一个有利于创新的技术知识环境以及在产学研合作方面提供更多的制度支持与资金支持。产业层次的经验分析表明,虽然我国技术创新产出增长很快,但与国外相比,目前我国产业技术创新水平普遍较低,虽然大量引进技术却不注意消化吸收,因而产业创新效率较低。需要破除体制障碍,提高产业开放程度和集聚程度,提高产业内部吸收能力,从而提高创新效率。东部地区的创新效率是中国最突出的,而在东部地区内部,上海、江苏、浙江、山东和广东这四省一市是我国技术创新投入和产出最突出的地区,也是中国产业集聚水平和经济发展水平最高的5个省市,因此,对这5省市进行区域经济发展的影响因素研究。研究表明,生产集聚与创新集聚的确有着密切的关系,而且与区域经济发展相关性也非常显著,但目前我国仍旧以生产带动经济发展为主。技术创新的带动作用在浙江、广东和上海等创新效率较高的省市发挥得比较明显,而在其他省市则难以体现。因此,本文结论认为,技术创新效率高的地区,技术创新对区域经济发展的动力作用也比较明显。总而言之,从技术创新投入到创新产出再到影响区域经济发展的过程中,都不能忽视对于技术创新效率的关注。无论是区域层次还是产业层次,技术创新过程中都存在高投入高产出却低效率的“效率悖论”,这提醒我们,应当注意技术创新的路径,从“投入导向型”转向“效率导向型”。技术创新方式的转变,不仅有利于提高技术创新的产出水平,而且对于区域经济发展水平也大有裨益。更多还原

【Abstract】 Followed with the new growth theory in which technology acts as a endogenous factor, the study on the endogenous technology innovation goes deepen. On the basis of dominate and foreign literatures, we focus on the empirical study on the innovation efficiency in districts and in industries and find the important roles of innovation efficiency specially in the roads from innovation inputs to outputs and to the economic development. This article can be linked with the new growth theory and provide China a feasible path from the large country to the power country in technology innovation.The main points are stated as below:The R&D approach of a firm is correlated with the technological and knowledge environment around the firm. On the condition that the firm want to maximum the innovation efficiency, the more technological opportunity and the knowledge, the more identified the R&D approach is. So , it is very important to provide a environment beneficial to innovation.In theoretical approach, the technological and knowledge environment is included in the three-stage game model. The model shows a firm’s R&D incentive is strengthened in order to absorb the environmental knowledge and therefore confirmed the two faces of R&D. At the same time, it conclude that in the superior enviroment, to increase the efficiency, the R&D approach of a firm has a trend to be identical with other firms. So the environment round the firm is very important to a firm.The innovation inputs, including R&D, endogenous absorbing capacity, technological opportunity, and the innovation outputs is not linear , with the influence of innovation efficiency , they form the "innovation trajectory , which confirms the advantage of what have had advantages and deepen the gap of superiors and inferiors, that is named "Mathew Effect" in innovation.In the technology innovation system, government, enterprises, colleges, and research institutes acted differently owing to the target and field of innovation. On the empirical study in the blocks and areas, we find the enterprises and colleges and government play prominent role in the eastern provinces and city which create the most efficient technology innovation. In China, generally speaking, colleges have a large effect in innovation system, enterprises have not been the main partner of innovation system, the role of colleges is very large and the institutes have the lowest effect. The efficiency of cooperation innovation of enterprises, colleges, and research institutes is yet not very strong. Therefore, a system should be build, in which enterprises act as the main innovator and cooperate with colleges and research institutes. The function of government is to provide a environment beneficial to innovation.The empirical study on the industries find , in comparison with foreign industries, our innovation efficiency is very low. Though large quantities of technology is imported, firms have not pay much attention on the absorbing, which lead to the low efficiency of innovation. Observing the influencing factors on technology innovation efficiency, I found that several factors such as the degree of industry open, the institutional innovation, and industrial agglomeration, have strong effect on the industrial innovation efficiency. So, in order to increase the efficiency of innovation, we can focus on these points.The eastern area of China have the most prominent efficiency , Shanghai, Jiangsu, Zhejiang, Shandong, and Guangdong, these five provinces and city in the eastern areas have the most inputs and innovation outputs, and at the same time, they have the highest agglomeration and economic development. The empirical study on the industries of these 5 provinces and cities confirm that economic development of some districts is due to production agglomeration but not innovation agglomeration. In Shanghai, Zhejiang, and Guangdong, we find the role of innovation agglomeration. Exactly, these three areas is just the three ones that are the most efficient provinces and city. So, we conclude that technology innovation is the engine of regional economic development specially in areas which innovation efficiency is high.On the whole, in the path from innovation inputs to innovation outputs and to regional economic development, the efficiency of technology innovation should not be neglected. In regional and industries empirical study, we all find the "efficiency paradox" that means the the efficiency is low though the inputs and outputs are all high. The paradox tell the truth that the innovation path should turn to "efficiency oriented" from "input oriented" model. This change would have positive impact on not only the innovation output but also the regional economic development.更多还原