# United Kingdom "LFS: In Employment: Professional, scientific & technical activities: : Women: Aged 16 and over: Thousands: NSA" ()

United Kingdom "LFS: In Employment: Professional, scientific & technical activities: : Women: Aged 16 and over: Thousands: NSA" () was down 0.4% year on year in Q2 2026, compared with −3.5% in Q1 2026.
The level stood at 1,355.0 thousands.
Over the past ten years it has ranged from −8.8% (Q3 2024) to 15.2% (Q4 2023), averaging 3.6%.

- Source agency: UK Office for National Statistics
- Makrognosis code: `UK.EMP.Q.NSA.ITEM-I4F6.MEASURE-LEVEL.SEX-F`
- Frequency: quarterly
- Units: thousands
- Coverage: 1997-01-01 to 2026-04-01 (118 observations)
- Page: https://makrognosis.robomacro.com/data/united-kingdom/lfs-in-employment-professional-scientific-and-technical-activities-uk-emp-q-nsa-item-i4f6-measure-level-sex-f

## Recent data

| Period | Value | Y/Y % |
|---|---:|---:|
| Q2 2026 | 1,355.0 thousands | −0.4 |
| Q1 2026 | 1,326.0 thousands | −3.5 |
| Q4 2025 | 1,353.0 thousands | −2.1 |
| Q3 2025 | 1,337.0 thousands | 2.5 |
| Q2 2025 | 1,361.0 thousands | 3.7 |
| Q1 2025 | 1,374.0 thousands | −3.8 |
| Q4 2024 | 1,382.0 thousands | −2.4 |
| Q3 2024 | 1,304.0 thousands | −8.8 |
| Q2 2024 | 1,313.0 thousands | −6.2 |
| Q1 2024 | 1,428.0 thousands | 8.2 |
| Q4 2023 | 1,416.0 thousands | 15.2 |
| Q3 2023 | 1,430.0 thousands | 13.1 |
| Q2 2023 | 1,400.0 thousands | 8.6 |
| Q1 2023 | 1,320.0 thousands | 5.0 |
| Q4 2022 | 1,229.0 thousands | 1.0 |
| Q3 2022 | 1,264.0 thousands | 4.2 |
| Q2 2022 | 1,289.0 thousands | 7.3 |
| Q1 2022 | 1,257.0 thousands | 2.4 |
| Q4 2021 | 1,217.0 thousands | 3.6 |
| Q3 2021 | 1,213.0 thousands | 6.2 |
| Q2 2021 | 1,201.0 thousands | 9.8 |
| Q1 2021 | 1,228.0 thousands | 9.2 |
| Q4 2020 | 1,175.0 thousands | 2.9 |
| Q3 2020 | 1,142.0 thousands | 2.1 |

## API

```
curl -H "X-API-Key: $MAKROGNOSIS_KEY" \
  "https://makrognosis.robomacro.com/v1/series/UK.EMP.Q.NSA.ITEM-I4F6.MEASURE-LEVEL.SEX-F"

# Python
import requests
r = requests.get("https://makrognosis.robomacro.com/v1/series/UK.EMP.Q.NSA.ITEM-I4F6.MEASURE-LEVEL.SEX-F",
                 headers={"X-API-Key": KEY})
obs = r.json()["observations"]
```