Are patents with female inventors under-cited? Evidence from text estimation

• 成果类型: Article
• 署名作者: V. Hochberg, Yael; Kakhbod, Ali; Li, Peiyao; Sachdeva, Kunal
• 署名单位: Rice University; National Bureau of Economic Research; University of California System; University of California Berkeley; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2026.104307
• 发表日期: 2026-09
• 页码: 104307
• 关键词: innovation; GENDER; patent; Machine Learning; big data; inference; gender-differences; INNOVATION EVIDENCE; RECOGNITION

摘要

Utilizing leading machine learning techniques to analyze the textual content and quality of patents, we demonstrate that patents with female lead inventors are under-cited relative to what would be expected had the lead inventor been male. Male inventors are the greatest contributors to the undercitation of patents with female inventors, followed by female inventors and male examiners, while female patent examiners appear to be even-handed. Using market reactions to patents suggests no average difference in market value by the inventor's gender. The results have potential implications for research conclusions that rely on citation-based assessments of patent quality.