Inventor Gender and Patent Undercitation: Evidence from Causal Text Estimation
70 Pages Posted: 21 Nov 2022 Last revised: 14 Aug 2023
Date Written: August 13, 2023
Implementing a state-of-the-art machine learning technique for causal identification from text data (C-TEXT), we document that patents authored by female inventors are under-cited relative to those authored by males. Relative to what the same patent would be predicted to receive had the lead inventor instead been male, patents with a female lead inventor receive 10% fewer citations. Patents with male lead inventors tend to undercite past patents with female lead inventors, while patent examiners of both genders appear to be more even-handed in the citations they add to patent applications. For female inventors, market-based measures of patent value load significantly on the citation counts that would be predicted by C-TEXT, but do not load significantly on actual forward citations. The under-recognition of female-authored patents likely has implications for the allocation of talent in the economy.
Keywords: Innovation, Gender, Patent, Machine Learning, Big Data, Causal Inference
JEL Classification: J16, J24, J71, O30, C13
Suggested Citation: Suggested Citation