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The knowledge cost function

机译：知识成本函数

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摘要

This paper contributes to the economics of knowledge with an analysis of the knowledge cost function, shedding light on the determinants of the large variance in the cost of knowledge across firms. The amount and the composition of external knowledge and the internal stocks of knowledge that firms can access and use in the generation of new technological knowledge help to reduce the costs of knowledge. The empirical analysis is based on a panel of companies listed on UK and the main continental Europe financial markets for the period 1995-2006, for which information about patents have been gathered. The econometric analysis of the costs of knowledge considers the unit costs of knowledge on the left hand side, and on the right hand side next to R&D expenditures, the stock of knowledge internal and external to each firm, and their size. In order to articulate the different facets of the external knowledge that is made accessible by proximity with firms collocated in the same region (NUTS2), we include variables proxying for regional variety, complementarity and similarity. The results confirm the Marshallian hypothesis that the size and composition of the stock of external knowledge play a key role in reducing the actual cost of the generation of new technological knowledge at the firm level. The evidence also sheds new light on the Schumpeterian hypothesis, suggesting that the size of the stock of internal knowledge helps in reducing the costs of knowledge, while costs of knowledge increase with the size of R&D expenditures and employment (C) 2015 Elsevier B.V. All rights reserved.
机译：本文通过对知识成本函数的分析，为知识经济学做出了贡献，阐明了企业间知识成本的巨大差异的决定因素。企业在产生新技术知识时可以获取和使用的外部知识的数量和组成以及内部的知识储备有助于降低知识成本。实证分析是基于1995年至2006年期间在英国和欧洲主要大陆金融市场上市的公司组成的小组，已收集了有关专利的信息。知识成本的计量经济学分析考虑了左侧的知识单位成本，右侧的R＆D支出，每个公司内部和外部的知识存量及其规模。为了阐明外部知识的不同方面，这些知识可以通过与位于同一地区的公司（NUTS2）邻近来获取，我们包括代表地区多样性，互补性和相似性的变量。结果证实了马歇尔假说，即外部知识储备的大小和组成在降低企业层面产生新技术知识的实际成本中起着关键作用。证据还为熊彼特假说提供了新的视角，表明内部知识储备的大小有助于降低知识成本，而知识成本则随着研发支出和就业人数的增加而增加（C）2015 Elsevier BV版权所有保留。

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