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Localized knowledge spillovers: Evidence from the spatial clustering of R&D labs and patent citations

机译:本地化知识溢出:R&D实验室和专利引用在空间上的聚类证据

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Buzard et al. (2017) show that American R&D labs are highly spatially concentrated even within a given metropolitan area. We argue that the geography of their clusters is better suited for studying knowledge spillovers than are states, metropolitan areas, or other political or administrative boundaries that have predominantly been used in previous studies. In this paper, we assign patents and citations to these newly defined clusters of R&D labs. Our tests show that the localization of knowledge spillovers, as measured via patent citations, is strongest at small spatial scales and diminishes with distance. On average, patents within a cluster are about two to four times more likely to cite an inventor in the same cluster than one in a control group. Of import, we find that the degree of localization of knowledge spillovers will be understated in samples based on metropolitan area definitions compared to samples based on the R&D clusters. At the same time, the strength of knowledge spillovers varies widely between clusters. The results are robust to the specification of patent technological categories, the method of citation matching, and alternate duster definitions.
机译:布扎德等。 (2017)表明,即使在给定的大都市区域内,美国的研发实验室在空间上也高度集中。我们认为,与先前研究中主要使用的州,都市区或其他政治或行政边界相比,其集群的地理环境更适合于研究知识溢出。在本文中,我们将专利和引用授予这些新定义的研发实验室集群。我们的测试表明,通过专利引用来衡量的知识溢出的本地化在较小的空间范围内最强,并且随着距离的减小而减小。平均而言,同一集群中的发明人被发明人引用专利的可能性是对照组中被授予专利的人的大约二到四倍。进口方面,我们发现与基于研发集群的样本相比,基于大都市区定义的样本中知识溢出的本地化程度将被低估。同时,集群之间知识溢出的强度差异很大。结果对于专利技术类别,引文匹配方法和替代除尘器定义的规范是可靠的。

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