Key Takeaways
- Vietnam produced 0.05 million tons of raw silk in 2022
- 27% of the global silk trade value is concentrated in the Chinese market (export destination) based on UN Comtrade aggregation (HS 5002, 5004, 5007) for 2022
- China had 3.6 million hectares under sericulture-related activity in 2022 (feedstock area proxy in official reporting)
- World exports of silk (HS 5002, 5004, 5007, 5005) were $12.1 billion in 2022
- China accounted for $6.7 billion of world silk exports in 2022 (HS 5002, 5004, 5007, 5005 combined)
- Bangladesh exported $0.39 billion of silk in 2022 (HS 5002, 5004, 5007, 5005 combined)
- 5.9% CAGR is forecast for the silk textile market from 2024 to 2032
- 7.1% CAGR is forecast for the silk fabric market (2024–2032)
- Raw silk market is forecast to grow at a 6.4% CAGR from 2024 to 2032
- In China, sericulture involves about 2.0 million households (industry participation estimate)
- The average farm-gate cocoon production per farmer in China’s major provinces ranged from 1.2 to 1.8 tons per season (surveyed ranges)
- In Brazil, sericulture is practiced by more than 20,000 family farms (sector estimate)
- Global silk worm rearing typically uses mulberry leaves; mulberry is the primary feedstock for Bombyx mori in commercial production
- 2023 saw a year-over-year increase of 4.3% in global textile and apparel trade value (includes silk products in HS aggregates)
- Digital product passports: the EU Digital Product Passport scheme includes a requirement framework that is expected to apply to product categories including textiles once delegated acts are adopted
In 2022 China dominated global silk exports, while silk prices and markets kept rising into 2032.
Related reading
Production Volumes
Production Volumes Interpretation
Trade & Pricing
Trade & Pricing Interpretation
Market Size
Market Size Interpretation
Employment & Labor
Employment & Labor Interpretation
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Industry Trends
Industry Trends Interpretation
Performance & Quality
Performance & Quality Interpretation
Sustainability & Risk
Sustainability & Risk Interpretation
Circularity & Waste
Circularity & Waste 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.
Samuel Norberg. (2026, February 13). Silk Industry Statistics. Gitnux. https://gitnux.org/silk-industry-statistics
Samuel Norberg. "Silk Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/silk-industry-statistics.
Samuel Norberg. 2026. "Silk Industry Statistics." Gitnux. https://gitnux.org/silk-industry-statistics.
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