Initial Jobless Claims
Get weekly Initial Claims for Unemployment Insurance for the United States from the Department of Labor.
Initial Jobless Claims counts the number of individuals filing for unemployment insurance for the first time in a given week. Because it is reported weekly (versus Nonfarm Payrolls’ monthly cadence, see get_nonfarm_payrolls) and captures layoffs essentially in real time, it is one of the most timely leading indicators of labor-market deterioration and a core component of the Conference Board’s Leading Economic Index.
No API key is needed: without a FRED API key the data comes from the Department of Labor’s weekly claims report directly, which gives the same figures, although FRED adds the latest weeks before the report includes them. When a FRED API key is set (see the fred_api_key parameter of the Economics class), it comes from FRED.
See definition: https://fred.stlouisfed.org/series/ICSA
Also known as: initial claims, new unemployment claims.
Initial Jobless Claims in Python or via the MCP
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_initial_jobless_claims as shown below.
from financetoolkit import Economics
economics = Economics(
start_date='2020-01-01',
end_date="2025-12-31",
)
economics.get_initial_jobless_claims()
Or, after setting up the Finance Toolkit MCP, ask:
How many initial jobless claims were filed in the United States in each week from 29 November to 27 December 2025?
Which returns:
| Date | United States |
|---|---|
| 2025-11-29 | 216000 |
| 2025-12-06 | 235000 |
| 2025-12-13 | 224000 |
| 2025-12-20 | 215000 |
| 2025-12-27 | 203000 |
Parameters
get_initial_jobless_claims accepts the following parameters.
| Parameter | Type | Description | Example |
|---|---|---|---|
rolling | int | The rolling window size to use for smoothing the data (simple moving average). Defaults to None. | 4 |
trailing | int | The trailing window size to use for summing the data over trailing periods. Defaults to None. | 4 |
growth | bool | Whether to return the growth data or the actual data. Defaults to False. | True |
lag | int | The number of periods to lag the data by. Defaults to 1. | 4 |
standardize | bool | Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. | True |
rounding | int | None | The number of decimals to round the results to. Defaults to None. | 2 |