Intellectual property rights play an increasingly vital role in listed companies, serving as a key indicator for assessing core competitiveness and market control capabilities. Existing research reveals that each additional patent held by a listed company increases its market value by several million yuan. Moreover, regions with stronger intellectual property protection demonstrate a greater contribution of patent output to market capitalization.
As part of CnOpenData’s patent series, the A-share Listed Companies Patent and Citation Data comprises three sub-tables: Patent Quantity Statistics, Patent Quality Statistics, and Patent Details. This dataset not only evaluates the patent performance of A-share listed companies from both quantitative and qualitative perspectives but also encompasses comprehensive patent information covering listed companies and their subsidiaries, affiliated companies, and joint ventures. It serves as a significant complement to CnOpenData’s patent series database.
The Patent Quantity Statistics and Patent Quality Statistics tables are subdivided based on patent applications and patent grants, respectively. The Patent Quality Statistics table further categorizes patents into three types: inventions, utility models, and designs. Citation and cited information is presented in the Patent Details section, organized into six modules: Invention Applications, Invention Grants, Utility Model Applications, Utility Model Grants, Design Applications, and Design Grants. Each module includes four tables: Basic Information, Citations, Cited-by, and Transaction Records (note: design patents lack citation, cited-by, and transaction tables).
Time Coverage
- Invention disclosures: 1990–2023 (by application publication date)
- Invention grants/Utility models/Designs: 1990–2023 (by grant publication date)
Literature Citing This Dataset
- Peng Yuanhuai, 2023: "Value Creation of Government Data Openness: From the Perspective of Enterprise Total Factor Productivity," Journal of Quantitative & Technological Economics, No. 9.
- Zhang Jinqing & Li Zihao, 2023: "Can High-Quality Corporate Development Predict Stock Market Performance?—Evidence from Machine Learning Methods," Studies of International Finance, No. 6.
- Shang Yuping, Pan Zhou & Meng Meixia, 2023: "Innovation Performance of China’s Polycentric Urban Spatial Strategy: Perspectives from Agglomeration Economies and Amenities," China Economic Quarterly, No. 3.
Data Structure Overview
Field Descriptions
A-share Listed Companies Patent Quantity Statistics
A-share Listed Companies Patent Quality Statistics
A-share Listed Companies Patent Details - Invention Applications
A-share Listed Companies Patent Details - Invention Grants
A-share Listed Companies Patent Details - Utility Model Applications/Grants
A-share Listed Companies Patent Details - Design Applications/Grants
Sample Data
Due to the extensive number of tables, this page displays only Patent Application Quantity Statistics, Invention Patent Application Quality Statistics, and Invention Application Patent Details. Other sections are available on their respective module branch pages on the left.
A-share Listed Companies Patent Application Quantity Statistics
A-share Listed Companies Invention Patent Application Quality Statistics
A-share Listed Companies Invention Application Patent Basic Information
A-share Listed Companies Invention Application Patent Citation Table
A-share Listed Companies Invention Application Patent Cited-by Table
A-share Listed Companies Invention Application Patent Transaction Table
Related Literature
- Josh L. and Amit S, 2021, "The Use and Misuse of Patent Data: Issues for Finance and Beyond", The Review of Financial Studies.
- Liu Qing & Xiao Baigao, 2023: "Labor Costs and Labor-Saving Technological Innovation: Evidence from AI Language Models and Patent Texts," Economic Research Journal, No. 2.
- Fang Xianming & Hu Ding, 2023: "Corporate ESG Performance and Innovation: Evidence from A-share Listed Companies," Economic Research Journal, No. 2.
Data Update Frequency
Annual updates