Data and Code for: M&A and Innovation: A New Classification of Patents

Principal Investigator(s): View help for Principal Investigator(s) Zhaoqi Cheng, Boston University; Ginger Jin, University of Maryland & NBER; Mario Leccese, University of Maryland; Dokyun Lee, Boston University; Liad Wagman, Illinois Institute of Technology

Version: View help for Version V1

Name	File Type	Size	Last Modified
data			04/07/2023 10:08:PM
notebooks			04/07/2023 10:08:PM
stata-codes			04/07/2023 10:08:PM
LICENSE.rtf	application/rtf	40.2 KB	04/07/2023 06:08:PM
README.docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	25.2 KB	04/07/2023 06:08:PM
~$README.docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	162 bytes	04/07/2023 06:08:PM

Project Description

Summary: 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 M&A 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.

Scope of Project

JEL Classification:
D40 Market Structure, Pricing, and Design: General
G30 Corporate Finance and Governance: General

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Published Versions

V1 [2023-04-28]

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