# Average Weekly Earnings: Total Private — Watertown-Fort Drum, NY (NSA)

Average Weekly Earnings: Total Private — Watertown-Fort Drum, NY (NSA) was up 12.6% year on year in September 2026, compared with 13.8% in August 2026.
The level stood at 1,159.1 USD/week.
Over the past ten years it has ranged from −7.4% (Mar 2020) to 20.5% (Oct 2023), averaging 4.8%.

- Source agency: U.S. Bureau of Labor Statistics (flat files)
- Makrognosis code: `US-48060.AWE.M.NSA.IND-05000000`
- Frequency: monthly
- Units: USD/week
- Coverage: 2011-01-01 to 2026-09-01 (189 observations)
- Page: https://makrognosis.robomacro.com/data/united-states/average-weekly-earnings-total-private-watertown-fort-drum-ny-nsa

## Recent data

| Period | Value | Y/Y % |
|---|---:|---:|
| September 2026 | 1,159.1 USD/week | 12.6 |
| August 2026 | 1,157.2 USD/week | 13.8 |
| July 2026 | 1,164.9 USD/week | 16.6 |
| June 2026 | 1,189.3 USD/week | 18.8 |
| May 2026 | 1,177.9 USD/week | 19.7 |
| April 2026 | 1,184.2 USD/week | 17.6 |
| March 2026 | 1,188.0 USD/week | 12.9 |
| February 2026 | 1,125.3 USD/week | 7.1 |
| January 2026 | 1,079.5 USD/week | 2.5 |
| December 2025 | 1,035.2 USD/week | −0.05 |
| November 2025 | 1,053.8 USD/week | 1.1 |
| October 2025 | 1,029.7 USD/week | −3.1 |
| September 2025 | 1,029.1 USD/week | −3.5 |
| August 2025 | 1,017.1 USD/week | 1.8 |
| July 2025 | 998.8 USD/week | −0.2 |
| June 2025 | 1,001.0 USD/week | 3.7 |
| May 2025 | 984.4 USD/week | 0.2 |
| April 2025 | 1,007.2 USD/week | 3.0 |
| March 2025 | 1,052.0 USD/week | 6.6 |
| February 2025 | 1,050.3 USD/week | 7.5 |
| January 2025 | 1,053.0 USD/week | 9.4 |
| December 2024 | 1,035.8 USD/week | 8.9 |
| November 2024 | 1,042.0 USD/week | 0.6 |
| October 2024 | 1,062.2 USD/week | −0.02 |

## API

```
curl -H "X-API-Key: $MAKROGNOSIS_KEY" \
  "https://makrognosis.robomacro.com/v1/series/US-48060.AWE.M.NSA.IND-05000000"

# Python
import requests
r = requests.get("https://makrognosis.robomacro.com/v1/series/US-48060.AWE.M.NSA.IND-05000000",
                 headers={"X-API-Key": KEY})
obs = r.json()["observations"]
```