The purpose of this paper is to explore the correlation between the technological proximity measures in three areas: USA, Japan and Europe. In each economic area, we use information from two international patent systems to construct the technological proximity for 240 international firms. In particular, we select firms’ patents from United States Patent and Trademarks Office (USPTO) data and European Patent Office (EPO) data. In order to compute the technological proximity, we follow the methodology developed by Jaffe (1986), where a technological vector is based on the distribution of patents of each firm across technology classes. Since the Jaffe distance assumes that spillovers only occur within the same technology class, but rules out spillovers between different classes, we develop also a distance measure which exploits the Mahalanobis norm to identify the distance between different technology classes based on the frequency that patents are taken out in different classes by the same firm, as in Lychagin, Pinkse, Slade and Van Reenen (2010). The contribution to the existing literature is to investigate the robustness of the technological proximity measure and the extent to which it may be affected by patent system features

Knowledge technological proximity: evidence from US and European patents

ALDIERI, Luigi
2013

Abstract

The purpose of this paper is to explore the correlation between the technological proximity measures in three areas: USA, Japan and Europe. In each economic area, we use information from two international patent systems to construct the technological proximity for 240 international firms. In particular, we select firms’ patents from United States Patent and Trademarks Office (USPTO) data and European Patent Office (EPO) data. In order to compute the technological proximity, we follow the methodology developed by Jaffe (1986), where a technological vector is based on the distribution of patents of each firm across technology classes. Since the Jaffe distance assumes that spillovers only occur within the same technology class, but rules out spillovers between different classes, we develop also a distance measure which exploits the Mahalanobis norm to identify the distance between different technology classes based on the frequency that patents are taken out in different classes by the same firm, as in Lychagin, Pinkse, Slade and Van Reenen (2010). The contribution to the existing literature is to investigate the robustness of the technological proximity measure and the extent to which it may be affected by patent system features
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/26145
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