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Regional Industrial Growth and Biopharma Patent Networks: Empirical Insights from the UK

Gao, Yuan, Zhu, Z. (2022) Regional Industrial Growth and Biopharma Patent Networks: Empirical Insights from the UK. Applied Network Science, (7). Article Number 77. E-ISSN 2364-8228. (doi:10.1007/s41109-022-00518-3) (KAR id:98023)

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The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speedy mRNA vaccine development were results of cross-border patenting collaboration, we have proposed in a previous work a new method to identify the cross-border collaborative regional centres in the patent networks, using a clustering comparison approach based on adjusted mutual information (AMI). In this paper, we focus on the UK industrial landscape. We use the UK bioscience and health technology sector statistics from 2015 to 2020 and look into the regional growth of each postcode area. We compare the top growth regions with the cross-border collaborative centres identified using AMI comparison at the postcode area level and find that both long-term and short-term AMI gains show an increase in the correlation with regional annual growth rates of firm numbers in the studied sectors from 2016 to 2020, and the increase is more consistent with the short-term AMI gain. We also found that areas more central in the long-term cross-regional R&D collaboration demonstrate a stronger association with more developed industrial settings indicated by more firms and, potentially more employment and turnover in the field. However, AMI gains are found to have negative correlations with the industrial growths as a sign of possible trade-offs of being central

Item Type: Article
DOI/Identification number: 10.1007/s41109-022-00518-3
Uncontrolled keywords: biopharmaceutical; medical technology; patent networks; cross-border; clustering comparison; adjusted mutual information
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (
Depositing User: Zhen Zhu
Date Deposited: 16 Nov 2022 15:32 UTC
Last Modified: 16 Feb 2023 12:22 UTC
Resource URI: (The current URI for this page, for reference purposes)
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