FinzData/macro-labor

US macro data: Labor (595 series)

595 labor series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-11-01 on) of each series' latest values. It is refreshed weekly.

import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-labor/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-labor/data/wide_weekly.parquet")  # one column per series

or datasets.load_dataset("FinzData/macro-labor"). Wide tables by frequency: wide_weekly (19 series), wide_monthly (473 series), wide_quarterly (70 series), wide_annual (32 series), wide_other (1 series). series.csv lists every series with its title, units, frequency and how far back the full history goes.

The full history, point in time, through one API

The FinzData API serves every series back to its first observation, every revision (what was known on any past date, so backtests do not peek at later revisions), and daily updates, in the same format:

import finzdata as fz                     # pip install finzdata
c = fz.Client()                           # free API key: https://api.finzdata.com/
c.macro("UNRATE")                        # full history, latest values
c.macro("UNRATE", vintage="first")       # as first released
c.macro("UNRATE", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="labor")          # the catalogue for this category

Most-watched series in this package

ID	Title	Units	Frequency	Full history from
UNRATE	Unemployment Rate	Percent	Monthly	1948
PAYEMS	All Employees, Total Nonfarm	Thousands of Persons	Monthly	1939
CIVPART	Labor Force Participation Rate	Percent	Monthly	1948
ICSA	Initial Claims	Number	Weekly, Ending Saturday	1967
LES1252881600Q	Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over	1982-84 CPI Adjusted Dollars	Quarterly	1979
CES0500000003	Average Hourly Earnings of All Employees, Total Private	Dollars per Hour	Monthly	2006
JTSJOL	Job Openings: Total Nonfarm	Level in Thousands	Monthly	2000
MANEMP	All Employees, Manufacturing	Thousands of Persons	Monthly	1939
U6RATE	Total Unemployed, Plus All Persons Marginally Attached to the Labor Force, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Persons Marginally Attached to the Labor Force (U-6)	Percent	Monthly	1994
CCSA	Continued Claims (Insured Unemployment)	Number	Weekly, Ending Saturday	1967
LNS11300060	Labor Force Participation Rate - 25-54 Yrs.	Percent	Monthly	1948
OPHNFB	Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers	Index 2017=100	Quarterly	1947
TEMPHELPS	All Employees, Temporary Help Services	Thousands of Persons	Monthly	1990
UEMP27OV	Number Unemployed for 27 Weeks & over	Thousands of Persons	Monthly	1948
CLF16OV	Civilian Labor Force Level	Thousands of Persons	Monthly	1947
EMRATIO	Employment-Population Ratio	Percent	Monthly	1948
NROU	Noncyclical Rate of Unemployment	Percent	Quarterly	1949
UNEMPLOY	Unemployment Level	Thousands of Persons	Monthly	1948
CE16OV	Employment Level	Thousands of Persons	Monthly	1947
ECIWAG	Employment Cost Index: Wages and Salaries: Private Industry Workers	Index Dec 2005=100	Quarterly	1980
LNS14000006	Unemployment Rate - Black or African American	Percent	Monthly	1972
LNS14024887	Unemployment Rate - 16-24 Yrs.	Percent	Monthly	1948
IC4WSA	4-Week Moving Average of Initial Claims	Number	Weekly, Ending Saturday	1967
AHETPI	Average Hourly Earnings of Production and Nonsupervisory Employees, Total Private	Dollars per Hour	Monthly	1964
CES9091000001	All Employees, Federal	Thousands of Persons	Monthly	1939

All 595 series: series.csv. Browse them at https://finzdata.com/macro/category/labor.

More from FinzData

• Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
• Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
• Pricing (history bundles from $195 once): https://finzdata.com/pricing
• Python client (open source): https://github.com/FinzData/finzdata-python

Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData. Data for research, not investment advice.