Key Takeaways
- In 2023, DEI initiatives correlated with 18.4% higher employee engagement in aftermarket firms.
- Firms with strong DEI saw 12.7% lower turnover in 2022 aftermarket.
- Innovation patents from diverse teams: 22% higher in aftermarket R&D, 2023.
- In 2023, median pay for women in aftermarket was 92% of men's, gap narrowed by 2.1% since 2020.
- Promotion rates for URM in aftermarket: 14.3% vs. 22.7% majority in 2022.
- Aftermarket pay equity audits: 68% of firms compliant in 2023.
- In 2023, Black or African American employees made up 10.2% of the automotive aftermarket workforce, compared to 13.6% U.S. population.
- Hispanic or Latino workers in aftermarket: 18.7% in 2022, highest among segments.
- Asian employees in aftermarket management: 4.1% in 2023.
- In 2023, women comprised 14.2% of the total workforce in the U.S. automotive aftermarket industry, up from 11.8% in 2020.
- Female representation in entry-level aftermarket technician roles stood at 8.7% in 2022, highlighting a significant gender gap.
- Among automotive aftermarket managers, women held 19.3% of positions in 2023, compared to 28.1% industry-wide average.
- In 2023, 87% of aftermarket firms offered mandatory inclusion training, reaching 92% employee participation.
- Employee resource groups (ERGs) for women in aftermarket: 65% firm adoption in 2022.
- Unconscious bias training completion: 84.3% in aftermarket leadership, 2023.
After DEI investment, aftermarket firms saw higher engagement, retention, innovation, and customer satisfaction.
DEI Outcomes and Trends
DEI Outcomes and Trends Interpretation
Equity in Compensation and Advancement
Equity in Compensation and Advancement Interpretation
Ethnic Diversity
Ethnic Diversity Interpretation
Gender Diversity
Gender Diversity Interpretation
Inclusion Programs and Training
Inclusion Programs and Training 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.
David Sutherland. (2026, February 13). Diversity Equity And Inclusion In The Automotive Aftermarket Industry Statistics. Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-automotive-aftermarket-industry-statistics
David Sutherland. "Diversity Equity And Inclusion In The Automotive Aftermarket Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/diversity-equity-and-inclusion-in-the-automotive-aftermarket-industry-statistics.
David Sutherland. 2026. "Diversity Equity And Inclusion In The Automotive Aftermarket Industry Statistics." Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-automotive-aftermarket-industry-statistics.
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