产业集群的结构和发展：交易，技术和知识溢出外文翻译

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1、中文 1978 字 The structure and evolution of industrial clusters: Transactions, technology and knowledge spillovers 1. Introduction Over recent years, the interrelationships between technology, innovation and industrial location behaviour have come to be seen as essential features of regional developmen

2、t. Much research and policy-thinking has been devoted to understanding the factors explaining why particular types of technologies appear to blossom in particular localities, and how this affects local economic growth. Lessons are often drawn from observations of particularly successful innovative r

3、egions as a means of re-modelling both industrial and regional policy. It will be argued in this paper that insufcient consideration is still devoted to both the nature of innovation processes and the structural conditions under which technical change occurs across space. In order to explain the obs

4、erved variety of geographical models, it is necessary to take into account the nature of new knowledge in different production sectors. In particular, technological regimes, industrial structures and organisational practices, as well as their dynamics, are often overlooked in favour of simplied and

5、stylised constructs, which appeal to consultants or government policy-makers wishing for easy answers to complex problems. An example of this is the literature promoting industrial clusters. This paper attempts to classify industrial clusters on the basis of the existing literature, trying in partic

6、ular to integrate transactions costs views and innovation and technology perspectives to give account of both the diversity of cluster structures and the multiplicity of their evolution paths. In doing so, the following questions are here indirectly addressed. How can we explain the variety and dist

7、inctiveness of geographically bounded industrial clusters?Why particular types of technologies tend to thrive in particular localities? How do different types of clusters evolve over time? The paper is structured into 10 sections. In the following sectionwe discuss the various hypotheseswhich exist

8、concerning innovation and geography. In Section 3 we outline the generally-held arguments regarding the relationship between geography and knowledge spillovers, and in Section 4 we present a transactions costs classication of different types of industrial clustering previously developed elsewhere, w

9、hich is explicitly based on the implicit assumptions underlying most of the existing literature on agglomeration and clustering phenomena. Such a classication is very informative regarding identifying the nature and organisational logic of clusters, and on this basis Section 5 of the paper addresses

10、 the limits of the hypothesised advantages of clustering by considering the effects of unintended knowledge ows. considering the effects of unintended knowledge ows. Section 6 then explains the limitations of the transactions costs view in analysing the processes of cluster evolution, whilst Section

11、 7 briey introduces evolutionary perspectives on technical and structural change. Such perspectives are adopted in Section 8 to extend the transactions costs classication proposed in Section 4,in order to give an account of the diversity and multiplicity of possible evolutionary paths of industrial

12、clusters. Section 9 uses selected empirical examples to show the importance of both transactions costs and knowledge regimes in explaining patterns of cluster development. Section 10 outlines some brief conclusions. 10. Conclusions In the light of the arguments presented in this paper, it becomes cl

13、ear that all industrial clusters can be characterised in terms of both transactions costs and relations characteristics as described in Table 1, and also in terms of technological regimes and knowledge characteristics along the lines depicted in Table 2. Our aim, as in all attempts to classify units

14、 of analysis by reducing the complexity of the whole population, was to maximise differences among the categories. However, as Pavitt himself said about his own taxonomy, themainweakness of our attempt “is the high degree of variance still found in each category” (Pavitt, 2000, p. xi). This is all t

15、he more true here as, while Pavitt approach was inductive and based on detailed empirical observation of individual units of analysis such as rms (Archibugi, 2001), ours is deductive, based on different streams of the literature on the geography of innovation, and it attempts to classify composite u

16、nits of analysis such as clusters. From theories of innovation and technical change we know that innovators will tend to emerge in locations where technological opportunities are the highest.When there are conditions of high opportunity, high appropriability and high cumulativeness, innovators will

17、tend to be geographically concentrated, giving rise to emergent clusters. Nonetheless, whether these types of situations will arise depend on the nature of knowledge in both the industry and the rms. Whereas technical knowledge tends to be prevalently tacit, complex and systemic, the transaction cos

18、ts- and knowledge-based arguments here suggest that, in some circumstances, the transfer of such knowledge will be facilitated via informal personal contacts and exchanges in situations where rms are geographically clustered. Conversely, geographical concentration will be far less important when the

19、 industry knowledge base is simple, well codied and conditions of low opportunity, low appropriability and low cumulativeness prevail. However, the possible alternative characteristics of clusters presented here indicates that technological and knowledge features alone are not a sufcient guide to th

20、e types of clusters that are likely to emerge, nor are industry characteristics. Rather, as we have seen, knowledge and innovation processes, organisational, rm and industryspecic characteristics, and institutional and governance settings, all play a role in explaining the diversity of industrial cl

21、usters and also their evolutionary trajectories. Indeed, as any single rm (particularly when large and multinational) can follow more than one technological trajectory (Pavitt et al., 1989), clusters may wellbe engaged in a prevalent but not exclusive trajectory at any given point of time. Process-b

22、ased classicatory attempts, such as that presented in this paper, help thus explain multiple trajectories and patterns of evolution. Once we account for innovation and knowledge creation processes, it becomes very difcult to apply simple stylised cluster constructs, because there is neither a repres

23、entative Marshallian rm nor an illustrative innovative cluster. Co-location therefore may or may not offer structures, organisations and institutions which improve the likelihood of local innovation. Understanding this diversity, and in particular both the transactions costs features and also the kn

24、owledge features of any particular cluster, should be the underlining base for any policy actions geared at nding actual solutions to particular regional or industrial problems. On this basis, our future research will follow a two-fold path: (1) extend dynamic comparisons among empirical cases, to h

25、ave feedbacks on the scope and limitations of our classicatory attempt； (2) achieve a workable denition of the appropriate unit of analysis for assessing knowledge spillovers, and ultimately drawing policy implications. 翻译二： 产业集群的结构和发展：交易，技术和知识溢出 1.引言 近年来，技术、创新和工业区位行为之间的相互关系已经被视为地区发展的重要特征。许多人致力于了解为何

26、特定类型的科学技术总是产生于特定区域，以及其产生是如何作用于当地经济发展，展开了很多调查研究和政策思考。基于特定成功的“改革创新”区案例的观察研究的课程，也被作 为重建产业政策和区域政策的一种方式。 本文将讨论，对于自然的创新过程和技术空间变更的产生所需结构条件两方面的考虑仍然有所欠缺不周全。为了解说我们所观察的各种地理模型，不同生产部门中新知识的属性也必须纳入考虑范围。尤其是科技体制，产业结构和组织实践以及它们的推动力，这些常常因为简化和程序化的构想而被忽视。这使得顾问和政府政策决策者希望为这些复杂的问题找到简单的解决途径。这其中之一就是用文献理论促进产业集群的发展。 本文试图在现有文献研究

27、的基础上对产业集群进行分类，尤其致力于将交易成本论和科技创新论两种不同的集 群结构和多样的发展途径整合起来。通过这样，这里间接地引出以下几个问题：我们应该如何解释地域为边界的产业集群的多样性和特殊性？为什么特定种类技术总是趋向于产生于特定区域呢？为什么不同类型的集群发展时间不同？ 本文分为 10 个部分。在以下的部分我们将讨论创新和地域方面不同的假设。在第三部分，我们将大致描述被广泛提出的地理位置和知识溢出之间关系问题的主要争论。在第四部分，我们将列出其他研究提出的按照交易成本对不同类型的产业集群进行分类。这里明显地是以其他现存的大多文献关于集群和集聚现象的假设为基础。这种分类方式全面 充分考

28、虑了集群的属性和组织逻辑。在这个基础上，本文第五部分通过对预期外的知识外流的考虑，来限制集群的所假设出的好处。第六部分之后则解释交易成本论关于集群演变进程的分析，同时第七部分介绍的是创新变革观关于技术和机构变化的观点。这个观点被应用于第八部分以扩展第四部分所提出的按照交易成本分类的方法，为了将产业集群的可能发展途径的多样性和差异性纳入考虑。第九部分选取了很多实证例子来表明交易成本和知识外衣两者对于集群发展模式的阐述的重要性。第十部分则简要总结出几点结论。 10. 结论 随着本文展示的各种争论观点的指引，所有的 产业集群的分类变得很明确了，可以根据表一描述的交易成本和双方联系特点来划分，也可以根

29、据表二所描述的几点技术体制和知识特征来划分。如同所有进行单位分析分类的人试图降低总体的复杂性一样，我们的目标是将各类别间的差异放到最大。然而，如帕维特自己说自己的分类法，最大的缺陷在于我们的尝试中“每种类别本身仍存在很大的差异性”（帕维特， 2000,）。这用在这里再合适不过了。然而，帕维特的研究途径是归纳性的，并且是以具体对个别单位的分析，比如说公司的实证研究为基础（阿尔基布吉， 2001）。而我们的研究主要是推导性的，基于地 理革新流派的不同文献研究。同时试图对集群之类的复合单位进行分类。 从科技革新论我们知道，革新者更偏好于产生于技术进步机会比较多的区域。当具备好机遇，高占有性以及不错的可累积性三个条件时，革新者则会倾向于注重地域方面因素，这会促进集群的产生。虽然如此，这些情形的产生还是要看产业和公司本身的知识属性。虽然科学知识大多倾于心照不宣的，复杂的和系统的，但这里交易成本和知识为基础的争论表明，在一定环境下，公司地域集群的情况中私人合同和交换信息能促进这些知识的交换。相反，当产业基础很薄弱，法律完善并且机遇很少，可占有性 和可累积性也很低时，对地理上的关注则远不如前面重要了。