# United States All employees, thousands, textile product mills, seasonally adjusted

United States All employees, thousands, textile product mills, seasonally adjusted was up 0.1% year on year in October 2026, compared with 1.3% in September 2026.
The level stood at 93.00 thousands.
Over the past ten years it has ranged from −25.0% (Apr 2020) to 20.8% (Apr 2021), averaging −2.0%.

- Source agency: U.S. Bureau of Labor Statistics (flat files)
- Makrognosis code: `US.EMP.M.SA.IND-32314000`
- Frequency: monthly
- Units: thousands
- Coverage: 1990-01-01 to 2026-10-01 (442 observations)
- Page: https://makrognosis.robomacro.com/data/united-states/all-employees-thousands-textile-product-mills-seasonally-adjusted

## Recent data

| Period | Value | Y/Y % |
|---|---:|---:|
| October 2026 | 93.00 thousands | 0.1 |
| September 2026 | 93.70 thousands | 1.3 |
| August 2026 | 93.50 thousands | 1.9 |
| July 2026 | 93.10 thousands | 2.8 |
| June 2026 | 93.70 thousands | −0.3 |
| May 2026 | 93.10 thousands | −0.6 |
| April 2026 | 93.90 thousands | 0.4 |
| March 2026 | 93.90 thousands | −0.1 |
| February 2026 | 94.20 thousands | −0.4 |
| January 2026 | 94.20 thousands | −0.6 |
| December 2025 | 93.90 thousands | −2.1 |
| November 2025 | 93.40 thousands | −2.7 |
| October 2025 | 92.90 thousands | −3.8 |
| September 2025 | 92.50 thousands | −4.3 |
| August 2025 | 91.80 thousands | −4.5 |
| July 2025 | 90.60 thousands | −7.9 |
| June 2025 | 94.00 thousands | −5.4 |
| May 2025 | 93.70 thousands | −6.5 |
| April 2025 | 93.50 thousands | −6.9 |
| March 2025 | 94.00 thousands | −4.1 |
| February 2025 | 94.60 thousands | −4.3 |
| January 2025 | 94.80 thousands | −4.0 |
| December 2024 | 95.90 thousands | −2.0 |
| November 2024 | 96.00 thousands | −2.4 |

## API

```
curl -H "X-API-Key: $MAKROGNOSIS_KEY" \
  "https://makrognosis.robomacro.com/v1/series/US.EMP.M.SA.IND-32314000"

# Python
import requests
r = requests.get("https://makrognosis.robomacro.com/v1/series/US.EMP.M.SA.IND-32314000",
                 headers={"X-API-Key": KEY})
obs = r.json()["observations"]
```