Key Takeaways
- 82% of freight firms launched women-focused mentorship in 2023, boosting retention by 11%
- 45% of major freight carriers implemented racial equity training by 2022 end
- Women in Trucking Association grew to 4,500 members with freight scholarships in 2023
- In freight pay equity audits of 2023, women earned 82.3% of male counterparts' median wages in trucking roles
- Black freight drivers experienced a 7.1% wage gap compared to white drivers in 2022, controlling for experience
- Hispanic warehouse workers in freight averaged $18.42/hour vs. $21.15 for non-Hispanics in 2023
- In 2023, women comprised just 12.4% of the total workforce in the U.S. freight and logistics sector, highlighting a persistent gender imbalance
- A 2022 survey found that only 7.2% of long-haul truck drivers in the freight industry were female, down from 8.1% in 2020
- Female representation in freight brokerage roles stood at 22.3% in 2023, the highest among freight subsectors but still lagging behind national averages
- In 2023 retention surveys, 68.4% of women in freight reported feeling included vs. 82.1% of men
- Black freight employees had a 22.3% higher voluntary turnover rate than whites in 2022
- Hispanic drivers' retention in freight firms averaged 2.1 years vs. 3.4 for others in 2023
- Black or African American workers constituted 12.7% of the freight trucking workforce in 2022, below the national labor force average of 12.1%
- Hispanic or Latino employees made up 22.4% of U.S. freight industry workers in 2023, concentrated in driving roles
- Asian Americans represented only 3.1% of freight logistics professionals in 2022
Freight firms expanded DEI programs in 2022 to 2023, but pay and representation gaps persist.
DEI Programs and Initiatives Statistics
DEI Programs and Initiatives Statistics Interpretation
Equity and Compensation Statistics
Equity and Compensation Statistics Interpretation
Gender Diversity Statistics
Gender Diversity Statistics Interpretation
Inclusion and Retention Statistics
Inclusion and Retention Statistics Interpretation
Racial/Ethnic Diversity Statistics
Racial/Ethnic Diversity Statistics 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.
Sophie Moreland. (2026, February 13). Diversity Equity And Inclusion In The Freight Industry Statistics. Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-freight-industry-statistics
Sophie Moreland. "Diversity Equity And Inclusion In The Freight Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/diversity-equity-and-inclusion-in-the-freight-industry-statistics.
Sophie Moreland. 2026. "Diversity Equity And Inclusion In The Freight Industry Statistics." Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-freight-industry-statistics.
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