M&A and Innovation: A New Classification of Patents

Zhaoqi Cheng, Ginger Zhe Jin, Mario Leccese, Dokyun Lee and Liad Wagman

AEA Papers and Proceedings, 2023, vol. 113, 288-93

Abstract: Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining mergers and acquisitions data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.

JEL-codes: D22 G34 G38 K21 L41 O31 O34 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1257/pandp.20231100

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