Inventor Gender and Patent Undercitation: Evidence from Causal Text Estimation

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

Hochberg, Yael V. and Kakhbod, Ali and Li, Peiyao and Sachdeva, Kunal, Inventor Gender and Patent Undercitation: Evidence from Causal Text Estimation (August 13, 2023). Available at SSRN: https://ssrn.com/abstract=4269703 or http://dx.doi.org/10.2139/ssrn.4269703