Explore the "CareerBuilder US Jobs Dataset – August 2021," a valuable resource for understanding the dynamics of the American job market.

This dataset features detailed job listings from CareerBuilder, one of the largest employment websites in the United States, and provides a comprehensive snapshot of job postings as of August 2021.

Key Features:

• Extensive Job Listings: Includes thousands of job postings across various industries and sectors, providing a broad view of employment opportunities in the US.
• Detailed Information: Each listing contains essential details such as job titles, company names, locations, job descriptions, employment types (full-time, part-time, contract), required qualifications, and salary ranges.
• Insights into Trends: Analyze trends in employment, including the most in-demand skills, top hiring companies, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research: This dataset is perfect for researchers, HR professionals, and data analysts interested in understanding the current state of the job market, developing recruitment strategies, or studying labor market dynamics.

By leveraging this dataset, you can gain valuable insights into the US job market as of August 2021, helping you stay ahead of industry trends and make informed decisions. Whether you're a job seeker, employer, or researcher, the CareerBuilder US Jobs Dataset offers a wealth of information to explore.

Job Growth Statistics: Statistics on job growth are essential in understanding the state and trajectory of an economy because they offer insight into the shifting dynamics of labor markets. By measuring net job addition or subtraction over a certain timeframe, employment growth statistics allow policymakers, companies, and individuals to make well-informed decisions regarding workforce planning, investment decisions, or career choices. Statistics on job growth provide a key measure of economic development as they show whether an economy is expanding, contracting, or remaining stable. Positive employment growth numbers often signal healthy economies with increased consumer spending and company confidence. Conversely, negative or stagnant job growth indicates a slowdown or recession. Furthermore, statistics on employment growth may also be used to highlight developing markets and professions for policymakers as well as job seekers in finding prospective development areas. As such, employment data provides an essential means of measuring an economy's current state and future direction, as well as helping shape policies and initiatives within it. Editor’s Choice From 2020-2030; job growth in the US is anticipated to be 5.3%. Nurse practitioners are predicted to experience the highest job growth; between 2021-2031 at 45.7%; 2019 alone saw sectors producing goods create 188,000 new jobs. Leisure and hospitality job creation decreased by 47% year-on-year between April 2020 and March 2021. President Clinton created 19 million new employment opportunities between June and July of 2022 and 528,000 nonfarm payroll employees were gained; yet by April 2020 20.5 million jobs had been lost from the economy as a whole. By 2031, it is projected that employment opportunities across the nation will reach 166.5 million; over that same timeframe childcare service workers have seen their ranks decline by 336,000. Since the COVID-19 outbreak, healthcare employment levels have suffered a dramatic decrease. By some accounts, over one and a half million employees may have left healthcare jobs since 2016. (Source: zippia.com)

In 2025, it was estimated that over 163 million Americans were in some form of employment, while 4.16 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

A record 4.4 million employees quit their jobs in September 2021, and many businesses are struggling to fill open positions. Although at a national level the labor market appears historically tight, we show that labor market tightness differs widely across states. Most states have tighter labor markets than before the pandemic, but others have struggled to recover.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33000 dwelling units.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Biographical information (marital status, education, etc.) - Economic activities in the last week for persons aged 15 years and older - Unemployment and economic inactivity for persons aged 15 years and above - Main work activity in the last week for persons aged 15 years and above - Earnings in the main job for employees, employers and own-account workers aged 15 years and above

From 2010 the income data collected by South Africa's Quarterly Labour Force Survey is no longer provided in the QLFS dataset (except for a brief return in QLFS 2010 Q3 which may be an error). Possibly because the data is unreliable at the level of the quarter, Statistics South Africa now provides the income data from the QLFS in an annualised dataset called Labour Market Dynamics in South Africa (LMDSA). The datasets for LMDSA are available from DataFirst's website.

Statistics Norway established the Labor Force Survey (LFS) in 1972, and it has been conducted quarterly ever since. The LFS measures the population's participation in the labor market and provides comprehensive information on unemployment, employment, people outside the labor force, temporary employees, underemployed and other subgroups that are not captured by register-based statistics. This makes the LFS one of the most important sources of information about conditions in the Norwegian labor market.

Right from the start, the aim has been to ensure that the survey is comparable with similar surveys internationally. Today, the LFS is designed in accordance with the EU's statistical regulations to ensure consistent and comparable European statistics. The LFS data contains long time series, and although there have been some breaks in the time series due to changes in the questionnaire and data collection, the most central variables have been continuously included since the start. This makes it possible to present time series data for the employed, unemployed and people outside the labor force all the way back to 1972.

From 2020 to 2021, a significant break in the time series was necessary due to the implementation of a new framework regulation for European social statistics. The production system was revised in full, with changes in sampling, weighting, use of register data, and a new questionnaire, several changes were made to modernize and streamline data collection.

The dataset consists of a single quarter, along with a quarterly weight. The dataset should be used for a quarterly average rather than an annual distribution. If an annual average is desired, separate datasets are available.

In the 3rd quarter of 2025, the employment rate in the United Kingdom was 75 percent, down from 75.3 percent in the previous quarter. After almost dropping to 70.1 percent in 2011, the employment rate in the United Kingdom started to climb at a relatively fast pace, peaking in early 2020. Due to the onset of the COVID-19 pandemic, however, employment declined to 74.6 percent by January 2021. Although not quite at pre-pandemic levels, the employment rate has since recovered. Labor market trouble in 2025? Although unemployment in the UK spiked at 5.3 percent in the aftermath of the COVID-19 pandemic, it fell throughout most of 2022, to just 3.6 percent in August 2022. Around that time, the number of job vacancies in the UK was also at quite high levels, reaching a peak of 1.3 million by May 2022. The strong labor market put employees in quite a strong position, perhaps encouraging the high number of resignations that took place around that time. Since 2023, however, the previously hot labor market has cooled, with unemployment reaching 4.6 percent in April 2025 and job vacancies falling to a four-year low of 736,000 in May 2025. Furthermore, the number of employees on UK payrolls has fallen by 227,500 in the first five months of the year, indicating that 2025 will be a tough one for the labor market. Headline economic measures revised in early 2025 Along with the unemployment rate, the UK's inflation rate is also expected to be higher than initially thought in 2025, reaching a rate of 3.2 percent for the year. The economy will also grow at a slower pace of one percent rather than the initial prediction of two percent. Though these negative trends are not expected to continue in the long term, the current government has already expended significant political capital on unpopular decisions, such as the cutting of Winter Fuel Payments to pensioners in 2024. As of June 2025, they are almost as unpopular as the previous government, with a net approval rating of -52 percent.

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2021 (LFS). The survey rounds covered a total sample of about 25,179 households (about 6,300 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2020.

---> Sampling Frame: The sampling frame consists of a comprehensive sample selected from the Population, Housing and Establishments Census 2017: This comprehensive sample consists of geographical areas with an average of 150 households, and these are considered as enumeration areas used in the census and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 8,040 households in each quarter of 2021.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 536 enumeration areas for the whole round. Second stage: we select a systematic random sample of 15 households from each enumeration area selected in the first stage.

---> Sample strata: The population was divided by: 1- Governorate (17 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 536 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2020 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. From the beginning of March 2020, with the spread of the COVID-19 pandemic and the home quarantine imposed by the government, the personal (face to face) interview was replaced by the phone interview for households who had phone numbers from previous rounds, and for those households that did not have phone numbers, they were referred to and interviewed in person (face to face interview). Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 32,160 households of which 25,179 households completed the interview; whereas 16,355 households from the West Bank and 8,824 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 79.8% while in the Gaza Strip it reached 90.5%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached 16.7% which is very low once compared to the

The contents of the dataset relate to employment and unemployment trends in the province of Trento. The data, which come from various sources, were compiled by the Labour Market and Policy Studies Office for the drafting of the Annual Employment Report in the province of Trento, available as open content at the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-and-society/Labour-market/Employment-and-unemployment/Year-2021 Data presented in absolute values shall be rounded to the nearest hundred. For this reason, the totals may not coincide with the sum of the individual values. The "time extension" metadata indicates the year (or years, in case of a time series) to which the dataset resources refer. In some cases, resources referring to a year may also contain data from the previous year for comparison. The indent ”-“ replaces the unpublished data as either unavailable or undeterminable or unpublishable to protect the confidentiality of the statistical data (for values less than or equal to 5) or, in the case of sampling values, unreliable. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated. ATTRIBUTION: data compiled by the Office of Studies of Policies and Labour Market on data of continuous survey on the annual average labour force Istat-ISPAT.

The size of the US labor force declined by 2.3 million people between December 2019 and December 2021. Our experts examine demographic changes to determine if this decline is a passing trend or if it’s here to stay.

Abstract

Focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp)

Analysis unit

Household, Individual.

Universe

The survey covered all the Palestinian persons aged 10 years and above who are a usual residence in State of Palestine

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of this survey is implemented periodically every quarter by PCBS since 1995, where this survey is implemented every quarter in the year (distributed over 13 weeks). The sample is a two-stage stratified cluster sample with two stages: First stage: selection of a stratified sample of 536 EA with (pps) method. Second stage: selection of a random area sample of 15 households from each enumeration area selected in the first stage. The estimated sample size in each quarter was 8,040 households in 2021.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The lfs questionnaire consists of four main sections: Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

All questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

The response rate was 85.0% in the fourth quarter 2021 The response rate was 83.3% in 2021 The response rate was 80.7% in the first quarter 2021 The response rate was 84.5% in the third quarter 2021 The response rate was 82.9% in the second quarter 2021

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and at the level of governorates of the West Bank and Gaza Strip.

Data appraisal

The concept of data quality encompasses various aspects, started with planning of the survey to how to publish, understand and benefit from the data. The most important components of statistical quality elements are accuracy, comparability and quality control procedures

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...

This dataset has been meticulously compiled and exported directly from the World Bank's official World Development Indicators (WDI) database, covering an extensive period from 2011 to 2021. It provides detailed socioeconomic indicators for 19 advanced economies: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Republic of Korea, Netherlands, Norway, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom, and the United States.

The dataset encompasses comprehensive indicators capturing detailed aspects such as: - Education Expenditure: Financial allocation towards primary, secondary, tertiary, and overall public education institutions. - Educational Attainment: Proportion of the adult population achieving various education levels, from primary education through doctoral degrees, distinguished by gender. - Health Expenditure: Overall and government-specific health expenditure as a percentage of GDP. - Healthcare Infrastructure: Availability of physicians, nurses, midwives, and hospital beds per capita. - Labor Market Dynamics: Employment indicators across agriculture, industry, and services sectors, employment-to-population ratios, and part-time employment statistics segmented by gender and age groups. - Labor Market Vulnerability: Data on vulnerable employment, self-employment, wage and salaried employment, and employer statistics by gender. - Youth Engagement: Rates of youth neither in education, employment nor training (NEET). - Migration Patterns: International migrant stock and net migration flows. - Healthcare Resources: Availability of healthcare professionals including physicians, nurses, midwives, and hospital beds per capita. - Health Expenditure: Overall healthcare spending as a percentage of GDP, including general government health expenditure. - Population Dynamics & Structure: Annual growth rates, total population figures, and gender-specific population distribution. - Research & Technological Infrastructure: Investment in research and development, density of researchers, and scientific publications. - Mortality and Survival: Survival rates to age 65, differentiated by gender. - Unemployment: Comprehensive unemployment data segmented by gender, education level (basic, intermediate, advanced), and youth-specific unemployment rates.

Files Descriptions:

This dataset includes a CSV file and a corresponding XLSX file: 1. Main Data CSV (WDI_MainData.csv): - Each row represents a specific country-year combination, structured as: - Time: Year (2011-2021) - Time Code: Numeric year code (integer) - Country Name: Name of the country - Country Code: ISO-3 country code - Columns for each indicator provided 2. Metadata XLSX (WDI_Metadata.xlsx): - Detailed descriptions for each indicator, including: - Code - License Type - Indicator Name - Short definition - Long definition - Source - Topic - Periodicity - Aggregation method - Statistical concept and methodology - Development relevance - Limitations and exceptions - General comments - Notes from original source - License URL

Aims of the Dataset:

This dataset provides insights into socioeconomic trends across 19 advanced economies from 2011 to 2021, enabling comparative analysis over time and between countries. It serves as a valuable resource for: - Comparing countries based on education, employment, healthcare, and demographics to identify trends and disparities. - Assisting students and professionals in selecting education and job destinations by analyzing relevant indicators. - Supporting policymakers in designing effective strategies by assessing labor markets, education systems, and healthcare investments. - Enabling researchers, analysts, and NGOs to evaluate public policies, workforce development, and socioeconomic conditions. - Facilitating data science and machine learning applications, including: - Data cleaning to prepare the dataset for further analysis. - Data visualization to explore trends and correlations across multiple indicators. - Feature engineering to extract meaningful patterns for predictive modeling. - Classification to categorize countries or time periods based on socioeconomic factors. - Trend analysis and forecasting to predict future changes in education, labor markets, and public health. - Anomaly detection to identify outliers and policy inefficiencies. - Automated dashboards to provide interactive and dynamic monitoring of key indicators. This dataset serves as a foundational tool for international benchmarking, decision-making, and AI-driven insights into socioeconomic dynamics.

Indicators (Sorted A-Z):

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The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

The domain 'Youth Employment' is based on the results of the European Union Labour Force Survey (EU-LFS).

The EU-LFS is a quarterly household sample survey carried out in the Member States of the European Union, EFTA countries (except for Liechtenstein) and Candidate Countries (Montenegro, North Macedonia, Serbia and Turkey). It is the main source of information about the situation and trends on the labour market in the European Union.

Since 1 January 2021, the EU-LFS is based on Regulation (EU) 2019/1700, also called the Integrated European Social Statistics Framework Regulation (IESS FR), and its Commission Implementing Regulation (EU) 2019/2240.

According to the regulations in force since 1 January 2021, the EU-LFS is organised in 9 topics:

• Technical items
• Person and household characteristics;
• Labour market participation;
• Educational attainment and background;
• Job tenure, work biography and previous work experience;
• Working conditions including working hours and working time arrangements;
• Participation in education and training;
• Health: status and disability, access to, availability and use of health care and health determinants;
• Income, consumption and elements of wealth, including debts.

The survey's target population consists of all persons in private households, although the variables related to labour market are only collected for persons aged 15-89 years and education and training for persons aged 15-74 years.

Detailed information on main features, legal basis, methodology and data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

Workforce Analytics Market witnessed USD 859 million in 2020, growing at a robust CAGR of 15.5% during forecast period.

Labour Force Survey The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. The LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. Objective The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Collection This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution. LFS revisions: Labour force surveys are revised on a periodic basis. The most recent revisions took place in 2025. As of January 2025, LFS microdata and estimates have been adjusted to reflect population counts from the 2021 Census, with revisions going back to 2011. Additionally, several changes were made to key variables on the PUMFs: Survey weights (FINALWT) have been updated to use 2021 Census population control totals. Sub-provincial geography (CMA) has been updated to the 2021 Standard Geographical Classification (SGC) boundaries. All industry data (NAICS_21) was revised to use the latest standard, North American Industry Classification System (NAICS) 2022. Coding enhancements were applied to improve longitudinal consistency of detailed National Occupational Classification data (NOC_10 and NOC_43). Data were revised to use the gender of person instead of sex (GENDER).