Key Takeaways
- After controlling for education, experience, and occupation, the US gender wage gap shrinks to 6.6% as of 2022 per Payscale.
- In the UK, the adjusted gender pay gap for full-time employees is 3.4% in 2023.
- OECD average adjusted pay gap is around 12-15% after controlling for hours, education, etc., in 2022 data.
- In US, young women under 30 earn 93% of men's unadjusted 2023.
- US women aged 25-34 with bachelor's earn 95% adjusted 2022.
- Mothers penalty 4% per child US 2023.
- In the US tech industry, women hold 26% of computing jobs but face 4% adjusted pay gap 2023.
- Finance and insurance sector US women earn 75% unadjusted but 5% adjusted 2022.
- Healthcare and social assistance US gap 18% raw, 2% adjusted 2022.
- US gender pay gap narrowed from 36% in 1980 to 17% in 2023.
- Since 2000, US gap closed by 5 points to 16.7% in 2023 BLS.
- EU gap reduced from 17.8% in 2011 to 12.7% in 2021.
- In the United States, full-time working women earned 83.3% of what full-time working men earned in 2023, resulting in a raw gender wage gap of 16.7 percentage points.
- Globally, women earned 77 cents for every dollar men earned in 2022 according to the World Economic Forum's Global Gender Gap Report.
- In the EU, the unadjusted gender pay gap stood at 12.7% in 2021, meaning women earned 87.3% of men's average earnings.
After accounting for education and experience, the adjusted US gender wage gap is just 6.6% in 2022.
Adjusted Gender Pay Gap
Adjusted Gender Pay Gap Interpretation
Demographic and Regional Variations
Demographic and Regional Variations Interpretation
Industry and Occupation-Specific Gaps
Industry and Occupation-Specific Gaps Interpretation
Trends and Policy Impacts
Trends and Policy Impacts Interpretation
Unadjusted Gender Pay Gap
Unadjusted Gender Pay Gap 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.
Alexander Schmidt. (2026, February 13). Gender Wage Gap Statistics. Gitnux. https://gitnux.org/gender-wage-gap-statistics
Alexander Schmidt. "Gender Wage Gap Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/gender-wage-gap-statistics.
Alexander Schmidt. 2026. "Gender Wage Gap Statistics." Gitnux. https://gitnux.org/gender-wage-gap-statistics.
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