Knowledge Flow between International Patent Classification Numbers and Knowledge Spillover Measures between Technology Sectors: Based on China??s Authorized Patent Data
Wang Gege, Liu Shulin
School of Economics, Wuhan University of Technology, Wuhan 430000
Abstract The new economic growth theory emphasizes the contribution of knowledge spillovers to economic growth. However, measuring knowledge spillovers has been a major challenge for economists due to its intangibility. With the advantage of big data, this paper analyzes knowledge spillovers by building a technical spillover matrix using the data of more than 300,000 of China’s authorized inventions that are in the incoPat Global Patent Database. This matrix uses the patents’ different IPC numbers to represent the knowledge flow between different technical fields. The main IPC number indicates the source technology field, the remaining sub IPC numbers represent the receiving technology field, and the four-digit IPC number is used to divide the technical sub-fields. The research discovers the externality of the knowledge spillover effect and identifies the direction, width, and intensity of the spillover among technologies. Consequently, the heterogeneity of the knowledge spillover in different technology fields is revealed. The conclusion of this paper can provide some theoretical support to the government’s subsidy policy for technology innovation.
Wang Gege,Liu Shulin. Knowledge Flow between International Patent Classification Numbers and Knowledge Spillover Measures between Technology Sectors: Based on China??s Authorized Patent Data[J]. 情报学报, 2020, 39(11): 1162-1170.
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