Key Takeaways
- Women held the gender pay gap at 16% globally in 2023, earning 84 cents for every dollar men earn
- In the US, median weekly earnings for women were $1,005 vs $1,226 for men in 2023
- EU unadjusted gender pay gap: 12.7% in 2022 data for 2023 reports
- 76% of companies with above-average financial returns have significant diversity in 2023, driven by women inclusion
- Firms in top quartile for gender diversity are 21% more likely to outperform on profitability in 2023
- 85% of US companies track gender diversity metrics in 2023, up from 60% in 2019
- In 2023, women comprised 47% of the US labor force
- Women's labor force participation rate globally stood at 49.6% in 2023
- In the EU, 68.9% of women aged 20-64 were employed in 2023
- Globally, women-founded startups received 2.3% of VC funding in 2022, amounting to $52 billion total VC but only $1.2 billion for women-led
- In the US, 17% of new businesses in 2023 were started by women, compared to 20% by men
- Women entrepreneurs in Latin America represent 33% of business owners in 2023
- In 2023, women held 10.4% of CEO positions in Fortune 500 companies, up from 6.4% in 2021
- Globally, only 8.8% of Fortune Global 500 CEOs were women as of 2023, compared to 4.8% in 2019
- Women occupy 29% of board seats in S&P 500 companies in 2023, an increase from 20% in 2014
Despite progress, women still earn less than men and remain underrepresented in leadership worldwide.
Compensation and Benefits
Compensation and Benefits Interpretation
Diversity and Inclusion Initiatives
Diversity and Inclusion Initiatives Interpretation
Employment and Workforce
Employment and Workforce Interpretation
Entrepreneurship and Startups
Entrepreneurship and Startups Interpretation
Executive Leadership
Executive Leadership Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Kevin O'Brien. (2026, February 13). Women In Business Statistics. Gitnux. https://gitnux.org/women-in-business-statistics
Kevin O'Brien. "Women In Business Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/women-in-business-statistics.
Kevin O'Brien. 2026. "Women In Business Statistics." Gitnux. https://gitnux.org/women-in-business-statistics.
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