The data set provides readers with data on the first half of the workforce for the years 2011 to 2020, per capita income for the first half of 2020 compared to 2019, and the unemployment rate in the working age. activities in the first half of the year from 2011 to 2020.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

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 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.

Unemployment Rate in China increased to 5.40 percent in February from 5.20 percent in January of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: November 2024 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: February 3, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2021 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

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 data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

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 continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

Canada LFS: Unemployment Rate: sa: Quebec data was reported at 5.300 % in Feb 2025. This records a decrease from the previous number of 5.400 % for Jan 2025. Canada LFS: Unemployment Rate: sa: Quebec data is updated monthly, averaging 9.000 % from Jan 1976 (Median) to Feb 2025, with 590 observations. The data reached an all-time high of 18.200 % in Apr 2020 and a record low of 3.800 % in Nov 2022. Canada LFS: Unemployment Rate: sa: Quebec data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.G021: Labour Force Survey: Unemployment. [COVID-19-IMPACT]

Unemployment in King County resulting from strategies to slow the spread of COVID-19

Unemployment Rate in Spain decreased to 10.61 percent in the fourth quarter of 2024 from 11.21 percent in the third quarter of 2024. This dataset provides the latest reported value for - Spain Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Unemployment: Female data was reported at 3,298.000 Person th in Feb 2025. This records an increase from the previous number of 3,234.000 Person th for Jan 2025. United States Unemployment: Female data is updated monthly, averaging 3,077.000 Person th from Jan 1948 (Median) to Feb 2025, with 926 observations. The data reached an all-time high of 11,494.000 Person th in Apr 2020 and a record low of 502.000 Person th in May 1953. United States Unemployment: Female data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G035: Current Population Survey: Unemployment. [COVID-19-IMPACT]

No description was included in this Dataset collected from the OSF

This datasets presents regional estimates of the unemployment rate of Local Government Area (LGA) regions for each quarter starting December 2010 up to September 2021. The boundaries used for the dataset follow the 2021 edition of the Australian Statistical Geography Standard (ASGS). Small Area Labour Markets (SALM) presents regional estimates of unemployment and the unemployment rate at two small area levels: Approximately 2,200 ABS SA2s, on a State/Territory and Metropolitan/Non-metropolitan basis. Estimates for the Capital City and the Rest of State are provided for the States and the Northern Territory. For approximately 540 Australian LGAs. The SALM Estimates have been smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is available on the Explanatory Notes page. Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution, particularly in regions where the SA4 level unemployment data are showing considerable volatility. As a result, quarter-to-quarter comparisons may not indicate actual movements in the labour market so we recommend using year-on-year comparisons. Even then, large movements in the SA2 and LGA data should be viewed with caution. The COVID-19 pandemic began to have a significant impact on the Australian labour market from March 2020, when Australia recorded its 100th COVID-19 case and the initial shutdown of non-essential services and trading restrictions took effect. Learn more about the dataset at the LMIP (Labour Market Information Portal). AURIN has spatially enabled the original data. Smoothed Estimates are not available for all SA2s and LGAs, for more information see the SALM 2016 ASGS Changeover User Guide.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.

The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail...

This dataset includes county-level COVID-19 cases and fatalities for all 50 U.S. states between January 21, 2020 and January 30th, 2021 as cumulative totals and by epi week. Standardized cases and fatalities are also calculated per 100,000 population. Data also includes county urban-rural designations, social vulnerability index (SoVI) values, community resilience values, unemployment change percentages, and coded county/state level COVID-19 mitigation value assignments. For more information on data manipulations or calculations, please reach out to corresponding author (Sarah L. Jackson - SJ36@email.sc.edu).

Unemployment Rate in the United Kingdom remained unchanged at 4.40 percent in January. This dataset provides the latest reported value for - United Kingdom Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This datasets presents regional estimates of the unemployment rate of Statistical Area Level 2 (SA2) regions for each quarter starting December 2010 up to June 2020. The boundaries used for the dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS). Small Area Labour Markets (SALM) presents regional estimates of unemployment and the unemployment rate at two small area levels: Approximately 2,200 ABS SA2s, on a State/Territory and Metropolitan/Non-metropolitan basis. Estimates for the Capital City and the Rest of State are provided for the States and the Northern Territory. For approximately 540 Australian LGAs. The SALM Estimates have been smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is available on the Explanatory Notes page. Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution, particularly in regions where the SA4 level unemployment data are showing considerable volatility. As a result, quarter-to-quarter comparisons may not indicate actual movements in the labour market so we recommend using year-on-year comparisons. Even then, large movements in the SA2 and LGA data should be viewed with caution. The COVID-19 pandemic began to have a significant impact on the Australian labour market from March 2020, when Australia recorded its 100th COVID-19 case and the initial shutdown of non-essential services and trading restrictions took effect. Learn more about the dataset at the LMIP. AURIN has spatially enabled the original data. Smoothed Estimates are not available for all SA2s and LGAs, for more information see the SALM 2016 ASGS Changeover User Guide.

France Unemployment Rate: Avg: sa: FM: Men: Age: 25 to 49 data was reported at 6.600 % in Dec 2024. This records a decrease from the previous number of 6.900 % for Sep 2024. France Unemployment Rate: Avg: sa: FM: Men: Age: 25 to 49 data is updated quarterly, averaging 6.900 % from Mar 1975 (Median) to Dec 2024, with 200 observations. The data reached an all-time high of 10.200 % in Jun 2015 and a record low of 1.700 % in Mar 1975. France Unemployment Rate: Avg: sa: FM: Men: Age: 25 to 49 data remains active status in CEIC and is reported by National Institute of Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G029: Unemployment Rate: Seasonally Adjusted: New Methodology. [COVID-19-IMPACT]

This datasets presents regional estimates of unemployment of Local Government Area (LGA) regions for each quarter starting December 2010 up to June 2020. The boundaries used for the dataset follow the 2019 edition of the Australian Statistical Geography Standard (ASGS). Small Area Labour Markets (SALM) presents regional estimates of unemployment and the unemployment rate at two small area levels: Approximately 2,200 ABS SA2s, on a State/Territory and Metropolitan/Non-metropolitan basis. Estimates for the Capital City and the Rest of State are provided for the States and the Northern Territory. For approximately 540 Australian LGAs. The SALM Estimates have been smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is available on the Explanatory Notes page. Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution, particularly in regions where the SA4 level unemployment data are showing considerable volatility. As a result, quarter-to-quarter comparisons may not indicate actual movements in the labour market so we recommend using year-on-year comparisons. Even then, large movements in the SA2 and LGA data should be viewed with caution. The COVID-19 pandemic began to have a significant impact on the Australian labour market from March 2020, when Australia recorded its 100th COVID-19 case and the initial shutdown of non-essential services and trading restrictions took effect. Learn more about the dataset at the LMIP. AURIN has spatially enabled the original data. Smoothed Estimates are not available for all SA2s and LGAs, for more information see the SALM 2016 ASGS Changeover User Guide.

This dataset presents data on the summary statistics of employment and population for metropolitan areas following the Greater Capital City Statistical Area (GCCSA) regions as of December 2020. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey. All statistics are 12-month averages of original data, December 2020. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16