Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Employment Rate in the United States increased to 59.70 percent in September from 59.60 percent in August of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides information on the unemployment rates for different demographic groups in the United States.

The data is sourced from the Economic Policy Institute’s State of Working America Data Library and economic research conducted by the Federal Reserve Bank of St. Louis.

The dataset contains unemployment rates for various age groups, education levels, genders, races, and more.

Interesting Task Ideas:

1. See how unemployment rates have changed for different groups of people over time.
2. Look into how education levels can affect unemployment rates.
3. Compare unemployment rates between different races / genders.
4. Check out how unemployment rates can vary across different age groups and genders.
5. Find out if there's a connection between education levels and unemployment rates within specific racial or gender groups.
6. Explore how economic downturns can impact unemployment rates for specific groups of people.
7. Use the data to create visuals that show how unemployment rates differ across all sorts of factors.

Don't forget to upvote this dataset if you find it useful! 😊💝

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Column Descriptions

Columns	Description
date	Date of the data collection. (type: str, format: YYYY-MM-DD)
all	Unemployment rate for all demographics, ages 16 and older. (type: float)
16-24	Unemployment rate for the age group 16-24. (type: float)
25-54	Unemployment rate for the age group 25-54. (type: float)
55-64	Unemployment rate for the age group 55-64. (type: float)
65+	Unemployment rate for the age group 65 and older. (type: float)
less_than_hs	Unemployment rate for individuals with less than a high school education. (type: float)
high_school	Unemployment rate for individuals with a high school education. (type: float)
some_college	Unemployment rate for individuals with some college education. (type: float)
bachelor's_degree	Unemployment rate for individuals with a bachelor's degree. (type: float)
advanced_degree	Unemployment rate for individuals with an advanced degree. (type: float)
women	Unemployment rate for women of all demographics. (type: float)
women_16-24	Unemployment rate for women in the age group 16-24. (type: float)
women_25-54	Unemployment rate for women in the age group 25-54. (type: float)
women_55-64	Unemployment rate for women in the age group 55-64. (type: float)
women_65+	Unemployment rate for women in the age group 65 and older. (type: float)
women_less_than_hs	Unemployment rate for women with less than a high school education. (type: float)
women_high_school	Unemployment rate for women with a high school education. (type: float)
women_some_college	Unemployment rate for women with some college education. (type: float)
women_bachelor's_degree	Unemployment rate for women with a bachelor's degree. (type: float)
women_advanced_degree	Unemployment rate for women with an advanced degree. (type: float)
men	Unemployment rate for men of all demographics. (type: float)
men_16-24	Unemployment rate for men in the age group 16-24. (type: float)
men_25-54	Unemployment rate for men in the age group 25-54. (type: float)
men_55-64	Unemployment rate for men in the age group 55-64. (type: float)
men_65+	Unemployment rate for men in the age group 65 and older. (type: float)
men_less_than_hs	Unemployment rate for men with less than a high school education. (type: float)
men_high_school	Unemployment rate for men with a high school education. (type: float)
men_some_college	Unemployment rate for men with some college education. (type: float)
men_bachelor's_degree	Unemployment rate for men with a bachelor's degree. (type: float)
men_advanced_degree	Unemployment rate for men with an advanced degree. (type: float)
black	Unemployment rate for the Black/African American demographic. (type: float)
black_16-24	Unemployment rate for Black/African American individuals in the age group 16-24. (type: float)
black_25-54	Unemployment rate for Black/African American individuals in the age group 25-54. (type: float)
black_55-64	Unemployment...

Labor Force Participation Rate in the United States increased to 62.40 percent in September from 62.30 percent in August of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Initial Jobless Claims in the United States decreased to 216 thousand in the week ending November 22 of 2025 from 222 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides values for UNEMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The American Time Use Survey (ATUS) provides nationally representative estimates of how, where, and with whom Americans spend their time, and is the only federal survey providing data on the full range of nonmarket activities, from childcare to volunteering.

For more information visit https://www.bls.gov/tus/

Graph and download economic data for Unemployment Rate in Los Angeles County, CA (CALOSA7URN) from Jan 1990 to Aug 2025 about Los Angeles County, CA; Los Angeles; CA; unemployment; rate; and USA.

This dataset presents labor market data, specifically employment and unemployment statistics in the United States of America. From a methodological point of view, it is an important tool for studying and analyzing the dynamics of the labor market and the economic state of the country. Let's take a look at its key aspects: Unemployment: This is the main indicator that indicates the number of people who are actively looking for work but cannot find it. Unemployment is an important economic indicator because it reflects the degree of utilization of a nation's labor potential and its economic stability. Labor force and its participation: The labor force includes those who are working or actively looking for work. Labor force participation measures what percentage of the civilian population is able and willing to participate in work. Employment and Employment Rate: Employment measures the number of people who have a job, while the employment rate shows the percentage of the workforce that is actually working. Duration of unemployment and the labor market: The dataset may also contain information on the duration of unemployment, which is an important aspect in assessing the efficiency of the labor market. Economic cycles and policy interventions: Data dynamics can indicate economic cycles and the effectiveness of policy interventions on labor and employment. This dataset can be useful to economists, human resource professionals, policy analysts and other researchers as it allows the study and analysis of key aspects of the labor market and unemployment, contributing to the understanding and forecasting of economic trends.

The number of people who are unemployed as a percentage of the active labour force (i.e. employed and unemployed).

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html

Percentage of unemployment insurance claimants filing continued claims who are black or African American.

This map displays data from the Selected Economic Indicators (DP03) dataset from the 2010 American Community Survey 5-Yr Estimates, U.S. Census Bureau. Data is shown at the level of Census Tract, County, and Small Area (aggregation of Census Tracts developed by the New Mexico Department of Health). Measuring poverty is a topic of much current discussion. See the following links: A Different Way to Measure Poverty - http://www.sanders.senate.gov/imo/media/image/census.jpg"Few topics in American society have more myths and stereotypes surrounding them than poverty, misconceptions that distort both our politics and our domestic policy making."They include the notion that poverty affects a relatively small number of Americans, that the poor are impoverished for years at a time, that most of those in poverty live in inner cities, that too much welfare assistance is provided and that poverty is ultimately a result of not working hard enough. Although pervasive, each assumption is flat-out wrong." -Mark Rank, Professor of Social Welfare at Washington University: http://opinionator.blogs.nytimes.com/2013/11/02/poverty-in-america-is-mainstream/

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show unemployment numbers and percentages by Regional Commission in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Percentage of unemployment insurance claimants filing continued claims who are American Indian or Alaska Native.

This study was undertaken to enable cross-community analysis of gang trends in all areas of the United States. It was also designed to provide a comparative analysis of social, economic, and demographic differences among non-metropolitan jurisdictions in which gangs were reported to have been persistent problems, those in which gangs had been more transitory, and those that reported no gang problems. Data were collected from four separate sources and then merged into a single dataset using the county Federal Information Processing Standards (FIPS) code as the attribute of common identification. The data sources included: (1) local police agency responses to three waves (1996, 1997, and 1998) of the National Youth Gang Survey (NYGS), (2) rural-urban classification and county-level measures of primary economic activity from the Economic Research Service (ERS) of the United States Department of Agriculture, (3) county-level economic and demographic data from the County and City Data Book, 1994, and from USA Counties, 1998, produced by the United States Department of Commerce, and (4) county-level data on access to interstate highways provided by Tom Ricketts and Randy Randolph of the University of North Carolina at Chapel Hill. Variables include the FIPS codes for state, county, county subdivision, and sub-county, population in the agency jurisdiction, type of jurisdiction, and whether the county was dependent on farming, mining, manufacturing, or government. Other variables categorizing counties include retirement destination, federal lands, commuting, persistent poverty, and transfer payments. The year gang problems began in that jurisdiction, number of youth groups, number of active gangs, number of active gang members, percent of gang members who migrated, and the number of gangs in 1996, 1997, and 1998 are also available. Rounding out the variables are unemployment rates, median household income, percent of persons in county below poverty level, percent of family households that were one-parent households, percent of housing units in the county that were vacant, had no telephone, or were renter-occupied, resident population of the county in 1990 and 1997, change in unemployment rates, land area of county, percent of persons in the county speaking Spanish at home, and whether an interstate highway intersected the county.

Continuing Jobless Claims in the United States increased to 1960 thousand in the week ending November 15 of 2025 from 1953 thousand in the previous week. This dataset provides the latest reported value for - United States Continuing Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Non Farm Payrolls in the United States increased by 119 thousand in September of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.