Get weekly Initial Claims for Unemployment Insurance for the United States from the Department of Labor (via FRED).

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.

Requires a free FRED API key, see the fred_api_key parameter of the Economics class.

See definition: https://fred.stlouisfed.org/series/ICSA

Also known as: initial claims, new unemployment claims.

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Calculate the Initial Jobless Claims in Python

The Initial Jobless Claims is available in the Economics module of the open-source Finance Toolkit. Install it 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', fred_api_key='FRED_API_KEY')

economics.get_initial_jobless_claims()

Which returns:

Date United States
2026-06-27 217000
2026-07-04 217000
2026-07-11 209000
2026-07-18 188000
2026-07-25 197000

Parameters

get_initial_jobless_claims accepts the following parameters:

  • rolling (int, optional): The rolling window size to use for smoothing the data (simple moving average). Defaults to None.
  • trailing (int, optional): The trailing window size to use for summing the data over trailing periods. Defaults to None.
  • growth (bool, optional): Whether to return the growth data or the actual data.
  • lag (int, optional): The number of periods to lag the data by.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Jobs & Society

Unemployment Rates Labour Productivity Income Inequality Population Poverty Rate Nonfarm Payrolls

The Economics module page introduces the module, and the sidebar lists all of its functions.

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