The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

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.

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. This data is based on a 12-month moving average.

This dataset is invaluable for data science applications due to its granularity and the historical depth it offers. With detailed monthly data on unemployment rates by age groups, data scientists can perform a myriad of analyses:

• Time Series Analysis: Analyzing trends over time to forecast future unemployment rates or identify cyclic patterns.
• Demographic Analysis: Understanding which age groups are most affected by unemployment and how this changes through economic conditions.
• Geographic Analysis: Although the dataset currently focuses on California, if similar data is available for other regions, comparisons can be made to understand regional economic differences.

The dataset can also be merged with other socioeconomic indicators like GDP, education levels, and industry growth metrics to examine broader economic narratives or policy impacts.

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.

Introduction

The U.S. job market, with its dynamic trends and fluctuating unemployment rates, serves as an important barometer for the nation's economic health. All rates provided in this dataset are seasonally adjusted. Delving into the intricacies of unemployment rates by age and gender helps researchers, policymakers, and analysts uncover underlying patterns and address potential disparities.

Usage Examples

• Economic Research: Study the historical unemployment trends to gauge economic cycles.
• Policy Making: Inform labor market policies and interventions based on age or gender disparities.
• Business Strategy: Companies can analyze job market conditions when considering expansions or contractions.
• Academic Projects: Students and educators can use the dataset for case studies, dissertations, or classroom projects.

Image Source Photo by Ron Lach : https://www.pexels.com/photo/woman-looking-for-jobs-in-newspaper-9832700/

Dataset Contents

This dataset, sourced from the FRED API, provides: - df_sex_unemployment_rates.csv: A breakdown of U.S. unemployment rates based on gender. - df_unemployment_rates.csv: Unemployment rates categorized by various age groups, ranging from young entrants (ages 16-17) to seasoned professionals (55 and above).

Together, these data files offer a comprehensive insight into the nuances of unemployment in the U.S., highlighting potential disparities in the job market across different age groups and between men and women.

Unemployment Rate in India remained unchanged at 5.20 percent in October. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains unemployment rates for the U.S. (1948 - Present) and California (1976 - Present). The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces. This rate is also defined as the U-3 measure of labor underutilization.

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! 😊💝

Checkout my other datasets

Pension Coverage in the USA

Non-High School Wage Penalty

Health Insurance Coverage in the USA

USA Hispanic-White Wage Gap Dataset

Black-White Wage Gap in the USA Dataset

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...

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.

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

Unemployment numbers and rates for those aged 16 or over. The unemployed population consists of those people out of work, who are actively looking for work and are available to start immediately. Unemployed numbers and rates also shown for equalities groups, by age, sex, ethnic group, and disability. The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics. The data are produced monthly on a rolling quarterly basis. The month shown is the month the quarter ends on. The International Labour Organization defines unemployed people as: without a job, want a job, have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks, or, out of work, have found a job and are waiting to start it in the next 2 weeks. The figures in this dataset are adjusted to compensate for seasonal variations in employment (seasonally adjusted). Data by equalities groups has a longer time lag and is only available quarterly from the Annual Population Survey, which is not seasonally adjusted. Useful links Click here for Regional labour market statistics from the Office for National Statistics. Click here for Labour market statistics from the Office for National Statistics. See here for GLA Economics' Labour Market Analysis. See here for Economic Inactivity statistics. See here for Employment rates. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

This dataset contains series for the Claimant Count (which measures the number of people claiming unemployment-related benefits) and vacancies.

Unemployment Rate in China decreased to 5.10 percent in October from 5.20 percent in September of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset from the Bureau of Labor Statistics provides monthly estimates regarding total employment and unemployment, which together comprise the labor force. The unemployment rate is the percentage of people in the labor force who are unemployed. Our data extract lists all data published for North Carolina’s counties from January 2019 to the present. This dataset is a comprehensive nationwide representation using estimates derived from the national Current Population Survey (CPS) and American Community Survey 5-year estimates. No disaggregations by demographic or worker characteristics are included in the unemployment rate estimate. Time series reports for each variable (employment, unemployment, and labor force) are available for each geography (county) using the BLS multi-screen data tool. Preliminary estimates are released within 30 days of each month and finalized within another 30 days, resulting in a 2-month data lag. The data is available for a variety of geographic areas, including states, MSAs, counties, cities and towns, and other census regions.

Unemployment numbers and rates for those aged 16 or over. The unemployed population consists of those people out of work, who are actively looking for work and are available to start immediately.

Unemployed numbers and rates also shown for equalities groups, by age, sex, ethnic group, and disability.

The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics.

The data are produced monthly on a rolling quarterly basis. The month shown is the month the quarter ends on.

The International Labour Organization defines unemployed people as: without a job, want a job, have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks, or, out of work, have found a job and are waiting to start it in the next 2 weeks.

The figures in this dataset are adjusted to compensate for seasonal variations in employment (seasonally adjusted).

Data by equalities groups has a longer time lag and is only available quarterly from the Annual Population Survey, which is not seasonally adjusted.

Useful links

Click here for Regional labour market statistics from the Office for National Statistics.

Click here for Labour market statistics from the Office for National Statistics.

See here for GLA Economics' Labour Market Analysis.

See here for Economic Inactivity statistics.

See here for Employment rates.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

Labour force participation rate: number of people aged 15+ in the labour force as a percentage of the working-age population (aged 15+). The notion of labour force refers to people who are employed or unepmployed (according to International Labour Organization). Unemployment rate: number of people aged 15+ who are unemployed as a percentage of people in the labour force. The notion of unemployment refers to people who are 1) not in employment, 2) available to work, 3) actively looking for work.

Find more Pacific data on PDH.stat.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Initial Claims for UI released by the CT Department of Labor. Initial Claims are applications for Unemployment Benefits. Initial Claims may not result in receiving UI benefits if the individual doesn't qualify. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

The initial claims reported in these tables are "processed" claims to the extent that duplicates and "reopened" claims have been eliminated. The claim counts in this dataset may not match claim counts from other sources.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on initial claims at the town level, see the dataset "Initial Claims for Unemployment Benefits by Town," here: https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

For data on continued claims see the following two datasets:

"Continued Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-in-Conn/f9e5-rn42

"Continued Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

Unemployment Rates by State

Unemployment rates in US states from 1976 to 2018

By Throwback Thursday [source]

About this dataset

This dataset, titled Unemployment by State 1976 - 2018, provides comprehensive information on unemployment rates in each state within the United States from 1976 to 2018. The data was sourced from the Bureau of Labor Statistics, ensuring reliability and relevance.

The dataset includes a variety of columns that provide valuable insights into the unemployment situation in each state during this time period. These columns include:

1. Month and Year: This column represents the specific month and year for which the data was recorded. It enables researchers to analyze trends and changes in unemployment rates over time.

2. State: This categorical column indicates the name of the individual state for which the unemployment data is recorded. It allows users to compare and contrast unemployment rates between different states throughout this time frame.

3. Civilian Population: This numeric column represents the total number of individuals who were not part of the military or institutionalized, and were at least 16 years old during a particular month and year. It serves as a reference point for understanding workforce size when analyzing employment trends.

4. Total Labor Force: This numeric column represents the total number of individuals who were either employed or actively seeking employment during a specific period in a particular state. It provides an accurate measure of workforce participation that can be used alongside other variables for further analysis.

5. Percent of Population: This numeric column reflects what percentage of a state's civilian population made up its total labor force during a given month and year, providing insights into labor market dynamics on a proportional scale.

6. Employed - Total: This numeric column gives an aggregate count representing how many individuals were employed within each state during a certain period, enabling analysis of employment opportunities across states over time.

7. Employed - Percent of Population: Expressed as a percentage, this variable indicates what proportion of each state's civilian population was employed during specific months/years under consideration. It helps measure the efficiency of state economies in utilizing available workforce resources.

8. Unemployed - Total: This numeric column represents the total number of individuals who were unemployed during specific months and years within each state. It serves as a quantifiable indicator of labor market fluctuations and economic challenges faced by different states at various times.

9. Unemployed - Percent of Labor Force: This numeric column reveals the percentage of total labor force members who were unemployed during specific periods, providing an insight into the severity of unemployment rates relative to overall workforce size.

With this comprehensive dataset, researchers can conduct in-depth analyses on unemployment rates across different states

How to use the dataset

This dataset provides comprehensive information on unemployment rates in each state from 1976 to 2018. The data is obtained from the Bureau of Labor Statistics and covers various aspects related to unemployment.

Here is a step-by-step guide on how to effectively utilize this dataset:

• Understand the columns: Familiarize yourself with the different columns present in the dataset. Each column represents a specific attribute related to unemployment rates, such as the month and year of the data recorded, state name, civilian population, total labor force, percentage of population employed or unemployed, and more.

• Filter by state: If you are interested in analyzing specific states, use the State column to filter and extract data for those particular states. This will help you focus your analysis on regions that are most relevant to your research or area of interest.

• Analyze trends over time: Utilize the Month and Year column (excluding dates) for understanding long-term trends in unemployment rates across different states. This can be done by creating line plots or bar charts comparing unemployment rates for multiple states over time.

• Compare employment percentages: The columns Employed - Percent of Population and Unemployed - Percent of Labor Force provide valuable insights into employment trends at both individual state as well as national levels. Use these percentages to compare different states' employment performances against each other or within a specific timeframe.

• Calculate raw numbers: The columns E...