# United Kingdom LFS: Employees: Cat T ISIC Rev. 4: : Women: Aged 15 and over: Thousands: NSA ()

United Kingdom LFS: Employees: Cat T ISIC Rev. 4: : Women: Aged 15 and over: Thousands: NSA () was up 3.8% year on year in Q2 2026, compared with −15.4% in Q1 2026.
The level stood at 27.00 thousands.
Over the past ten years it has ranged from −63.6% (Q3 2019) to 185.7% (Q4 2024), averaging 7.8%.

- Source agency: UK Office for National Statistics
- Makrognosis code: `UK.EMP.Q.NSA.GRP-EMPLOYEE.MEASURE-LEVEL.NACE-C-T.SEX-F`
- Frequency: quarterly
- Units: thousands
- Coverage: 2001-01-01 to 2026-04-01 (102 observations)
- Page: https://makrognosis.robomacro.com/data/united-kingdom/lfs-employees-cat-t-isic-rev-4-women-aged-15-and-over-thousands-nsa

## Recent data

| Period | Value | Y/Y % |
|---|---:|---:|
| Q2 2026 | 27.00 thousands | 3.8 |
| Q1 2026 | 22.00 thousands | −15.4 |
| Q4 2025 | 15.00 thousands | −25.0 |
| Q3 2025 | 19.00 thousands | 26.7 |
| Q2 2025 | 26.00 thousands | 52.9 |
| Q1 2025 | 26.00 thousands | 100.0 |
| Q4 2024 | 20.00 thousands | 185.7 |
| Q3 2024 | 15.00 thousands | 0.0 |
| Q2 2024 | 17.00 thousands | 41.7 |
| Q1 2024 | 13.00 thousands | −13.3 |
| Q4 2023 | 7.00 thousands | −61.1 |
| Q3 2023 | 15.00 thousands | −42.3 |
| Q2 2023 | 12.00 thousands | −50.0 |
| Q1 2023 | 15.00 thousands | −28.6 |
| Q4 2022 | 18.00 thousands | 0.0 |
| Q3 2022 | 26.00 thousands | 8.3 |
| Q2 2022 | 24.00 thousands | −20.0 |
| Q1 2022 | 21.00 thousands | 0.0 |
| Q4 2021 | 18.00 thousands | 12.5 |
| Q3 2021 | 24.00 thousands | 41.2 |
| Q2 2021 | 30.00 thousands | 172.7 |
| Q1 2021 | 21.00 thousands | 90.9 |
| Q4 2020 | 16.00 thousands | 23.1 |
| Q3 2020 | 17.00 thousands | 112.5 |

## API

```
curl -H "X-API-Key: $MAKROGNOSIS_KEY" \
  "https://makrognosis.robomacro.com/v1/series/UK.EMP.Q.NSA.GRP-EMPLOYEE.MEASURE-LEVEL.NACE-C-T.SEX-F"

# Python
import requests
r = requests.get("https://makrognosis.robomacro.com/v1/series/UK.EMP.Q.NSA.GRP-EMPLOYEE.MEASURE-LEVEL.NACE-C-T.SEX-F",
                 headers={"X-API-Key": KEY})
obs = r.json()["observations"]
```