Summary of Key Findings on Gender and Patenting in the U.S.

Population Representation vs. Patenting:

• Women represent approximately 51% of the U.S. population (U.S. Census Bureau, 2023).
• Women constitute about 47% of the U.S. workforce.
• However, within 1.78 million distinct U.S. inventors, only 13.1% (232,534) are women.
• Of 3.7 million U.S. patents analyzed:
  • 17.6% (651,327) include at least one female inventor.
  • 84.7% of these patents include mixed-gender teams (women + men).
  • Only 2.7% are from all-women inventor teams.

Trends Over Time (1976 to 2021):

• Patents involving at least one female inventor increased from 4.4% (1976) to 25.8% (2021).
• Growth is primarily driven by mixed-gender teams, reaching 22.8% in 2021.
• Patents from all-women teams remain rare, consistently below 3%, with a decline noted in the 2000s.
• Female inventors’ share increased from <2% in 1976 to 12%+ in 2021.
• Growth rate slowed recently: 3.5% (1980) → 9.1% (2000) → 11.8% (2020).

Impact of Gender Composition on Patent Characteristics:

• Highly significant relationship between team gender makeup and patent features such as reliance on science and use of public support.
• Science Citations:
  • All-male teams cite 4.3 scientific papers on average.
  • All-female teams cite 6.2 papers (≈44% more).
  • Mixed-gender teams cite 13.2 papers on average, the highest by far.
• Regression models (Model #1 and Model #2) confirm that patents involving women tend to cite significantly more scientific literature.
• Mixed-gender teams also better leverage public support mechanisms (details would be found in actual tables and figures referenced, e.g., Table 4, Fig. 3).

Notes for Reporting or Further Study

• The gender gap in patenting is stark despite increased female workforce participation.
• Mixed-gender collaboration appears most effective in integrating scientific knowledge.
• All-women teams remain an underutilized resource in innovation.
• The slow progress in closing the gender gap in recent decades points to persistent structural or institutional barriers.
• Further analysis of regression outputs and time series plots (from tables and figures referenced) would provide deeper insights.

If you want, I can help you prepare:

• A detailed executive summary
• Visualizations based on this data (e.g., recreate or enhance Fig. 2 trends)
• Suggestions for policy interventions or diversity initiatives

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