Working Paper: NBER ID: w31592
Authors: Yael Hochberg; Ali Kakhbod; Peiyao Li; Kunal Sachdeva
Abstract: 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: patents; gender; citation; machine learning; causal inference
JEL Codes: C13; J16; J24; J71; O30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| gender of inventor (J16) | patent citation counts (O34) |
| patents authored by female inventors (O31) | patent citation counts (O34) |
| male authors' behaviors (J16) | undercitation of female patents (J16) |
| female authors' behaviors (B54) | citation practices (Y50) |
| patent quality (L15) | patent citation counts (O34) |