Key Takeaways
- In US construction, men comprised 88.9% of employees in 2023, with women at 11.1%
- US construction workers aged 25-54 made up 65.2% of the workforce in 2023
- Hispanic or Latino workers held 34.1% of US construction jobs in 2023, highest ethnicity share
- In California, USA, construction employment was 927,000 in August 2024, the highest among states
- Texas construction jobs totaled 834,000 in August 2024, driven by infrastructure and housing booms
- Florida's construction sector employed 612,000 in August 2024, up 5.1% year-over-year
- Residential construction subsector in the US employed 4.1 million in September 2024, 51% of total construction jobs
- Nonresidential building construction in US had 1.15 million employees in September 2024
- Heavy and civil engineering construction employed 1.02 million in US September 2024
- In the United States, construction employment reached 8,017,000 in September 2024, marking a record high with a 4.2% year-over-year increase
- The European Union's construction sector employed 14.2 million people in 2023, representing 6.3% of total EU employment
- India's construction industry employed over 76 million workers in 2023, accounting for 14% of the national workforce
- US construction employment grew 3.8% year-over-year to September 2024, adding 296,000 jobs
- EU construction employment increased 1.2% from 2022 to 2023, recovering post-COVID
- India construction jobs grew 8.5% annually from 2018-2023, projected to add 10 million by 2027
Construction work remains male dominated and aging, while employment levels rise in the US and EU.
Related reading
Demographic Characteristics
Demographic Characteristics Interpretation
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Regional Variations
Regional Variations Interpretation
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Sector-Specific Employment
Sector-Specific Employment Interpretation
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Total Employment
Total Employment Interpretation
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Trend and Growth Data
Trend and Growth Data 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.
Priya Chandrasekaran. (2026, February 13). Construction Industry Employment Statistics. Gitnux. https://gitnux.org/construction-industry-employment-statistics
Priya Chandrasekaran. "Construction Industry Employment Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/construction-industry-employment-statistics.
Priya Chandrasekaran. 2026. "Construction Industry Employment Statistics." Gitnux. https://gitnux.org/construction-industry-employment-statistics.
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