This dataset provides the number of labor force participants in the State of Qatar, classified by employment sector: private, government, mixed, and domestic. It includes total workforce figures and the annual growth rate by sector. This dataset supports analysis of labor market structure and trends across major employment sectors.

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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The WIA [Workforce Investment Act of 1998] Adult and Dislocated Worker Programs Gold Standard Evaluation, provides findings on participant outcomes 30-months after random assignment under the evaluation. The evaluation began in 2008 and used a random assignment design to examine the impact of higher-tiered services provided by the Adult and Dislocated Worker programs in WIA, as implemented by 28 randomly selected local workforce investment areas (LWIAs) operating nationwide. The analysis describes the impact of different services provided under WIA core and intensive; and core, intensive and training. Researchers followed more than 34,000 study participants after random assignment collecting outcome data through follow-up surveys at 15 and 30 months and the National Directory of New Hires (an administrative database containing information on earnings and employment) at 36 months after random assignment.

description: The PWSD is a dataset that can be used to answer questions about various public workforce system programs and how these programs fit in with the overall public workforce system and the economy. It was designed primarily to be used as a tool to understand what has been occurring in the Wagner-Peyser program and contains data from quarter 1 of 1995 through quarter 4 of 2008. Also, it was designed to understand the relationship and flow of participants as they go through the public workforce system. The PWSD can be used to analyze these programs both individually and in combination. The PWSD contains economic variables, Unemployment Insurance System data, and data on programs funded by the Workforce Investment Act and Employment Service. Economic variables included are labor force, employment, unemployment, unemployment rate, and gross domestic product data.; abstract: The PWSD is a dataset that can be used to answer questions about various public workforce system programs and how these programs fit in with the overall public workforce system and the economy. It was designed primarily to be used as a tool to understand what has been occurring in the Wagner-Peyser program and contains data from quarter 1 of 1995 through quarter 4 of 2008. Also, it was designed to understand the relationship and flow of participants as they go through the public workforce system. The PWSD can be used to analyze these programs both individually and in combination. The PWSD contains economic variables, Unemployment Insurance System data, and data on programs funded by the Workforce Investment Act and Employment Service. Economic variables included are labor force, employment, unemployment, unemployment rate, and gross domestic product data.

This dataset includes data for an analysis of labor demand characteristics and workforce training needs in the metropolitan areas of La Paz-El Alto, Cochabamba, and Santa Cruz—large cities in Bolivia (Related publication only available in Spanish). This information is contrasted with a sample from intermediate and small cities in the country. Labor demand data for large cities comes from a survey of companies conducted in 2015 and 2016, while data for intermediate and small cities is derived from a survey conducted between 2016 and 2017. The document presents key findings on the productive characteristics of cities, company profiles, and workforce dynamics, including recruitment and selection processes, employee turnover, reasons for dismissals, training, demand for and valuation of skills, among other factors. Finally, it outlines policy implications for Bolivia’s labor market.

This dataset presents the number of individuals aged 15 years and above in the labor force, classified by major occupation groups. The values are reported in thousands and allow for comparison across time and occupational categories. This data supports labor market analysis, workforce planning, and policy evaluation.

Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

What Makes This Job Postings Data Unique

AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intelligence.

About Canaria Inc. ...

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Europe Workforce Management Software Market Size 2025-2029

The workforce management software market in Europe size is forecast to increase by USD 819.8 million at a CAGR of 6.8% between 2024 and 2029.

The Workforce Management Software market is experiencing significant growth, driven by the increasing need to optimize and organize the use of workforces. This trend is fueled by the rising adoption of digital HR technology, enabling businesses to streamline operations, enhance productivity, and improve employee engagement. However, the high cost of implementation and maintenance remains a challenge for many organizations, necessitating careful consideration and strategic planning. Additionally, the adoption of advanced workforce analytics, particularly those leveraging Machine Learning, is increasing.

By leveraging advanced features such as real-time attendance tracking, automated scheduling, and predictive analytics, businesses can effectively manage their workforce, reduce labor costs, and ensure compliance with labor regulations. The market is expected to continue its growth trajectory, offering substantial opportunities for companies and investors alike. Smartphone adoption is another trend driving the market growth.

What will be the Size of the market During the Forecast Period?

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In the dynamic world of workforce management, automated scheduling and HR automation solutions continue to gain traction, streamlining labor management and optimizing workforce planning. Time clock systems, including punch clock and mobile time tracking apps, ensure accurate attendance records and payroll processing. Predictive modeling and data analysis enable businesses to anticipate labor costs and adjust staffing levels accordingly. Employee databases, self-service portals, and communication tools foster better employee engagement and work-life balance initiatives. Big data and business intelligence enable effective talent acquisition, skills management, and performance reviews. Geo-location tracking and shift bidding facilitate efficient scheduling and labor management. Mobile phone users increasingly seek devices capable of leveraging 5G network technologies, with chipmakers responding by producing 5G chips for integration into mobile handsets.

Cloud computing-based workforce management platforms offer real-time access to employee data, enabling effective payroll processing, labor cost monitoring, and succession planning. Employee experience and wellness initiatives are increasingly integrated into these platforms, enhancing productivity and overall employee satisfaction. Employee productivity and performance are key areas of focus, with time tracking software and employee engagement tools offering valuable insights. Human resource management solutions, including payroll processing and employee communication, ensure regulatory compliance and streamline HR operations. In summary, the workforce management market is characterized by continuous innovation, with a focus on automation, data-driven decision making, and employee engagement. Improved hardware and software capabilities enable advanced digital functions such as web browsing, music, video, gaming, and camera capability. These trends are shaping the future of workforce management, offering businesses the tools they need to effectively manage their most valuable asset: their employees.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud based
  On-premises


End-user

  IT and telecom
  BFSI
  Healthcare
  Manufacturing
  Others


Business Segment

  Large enterprises
  SMEs


Geography

  Europe

    France
    Germany
    Italy
    UK

By Deployment Insights

The cloud based segment is estimated to witness significant growth during the forecast period. Cloud-based workforce management software is gaining traction in the business world as an alternative to traditional on-premise solutions. With the rise of remote work and mobile workforces, the flexibility and accessibility offered by cloud-based systems have become increasingly valuable. These solutions allow for real-time data access, enabling labor forecasting, time off management, and employee engagement. Human resources (HR) processes such as employee onboarding, training, and performance management can also be streamlined. Cloud-based systems offer several advantages over on-premise solutions. Predictable expenses, as payments are made regularly instead of large upfront investments and periodic maintenance fees. No powerful local server is required, reducing IT personnel costs. Cloud computing plays a crucial role in enabling thes

Analysis of ‘Labor Force Status by Race and Ethnicity: Beginning 2012’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5d5ae00f-3f59-49d9-bd5f-c5a44e0823bf on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset shows the population, civilian labor force, unemployed, and unemployment rate for people aged 16 years and older by race and ethnicity in New York State and its Labor Market Regions..

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Selected Labor Force Characteristics of Youth Aged 16 to 24’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d1294de6-d3da-499e-b7c3-d5d00e8b7e23 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset shows the population, civilian labor force, unemployed, and unemployment rate for people aged 16 to 24 years in New York State and its Labor Market Regions.

--- Original source retains full ownership of the source dataset ---

This layer shows full-time, year-round vs. part-time employment by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 65+ who worked in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B23027 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

Jobs are essential for the growth of individuals and countries alike. Achieving personal fulfillment is harder without a job, just as an economy as a whole cannot develop without the impetus of the labor market. These two perspectives unquestionably go hand in hand: from the individual perspective, finding a good job is a legitimate aspiration for anyone who wishes to support oneself and one's family; from the societal perspective, creating more and better jobs is essential to the achievement of lasting and equitable growth. Jobs for Growth rests on this dual vision. This book examines the performance of the region's labor market and, based on this analysis, proposes an integrated package of measures for both personal growth (through successful career paths) and economic growth (through more high-quality jobs and higher productivity). Over the past two decades, the bullish economic cycle has yielded undeniable gains for labor markets in Latin America and the Caribbean (LAC), among them lower unemployment, improved job creation, and a substantial increase in wages. However, the situation on the horizon -stagnation of the region's growth and weaknesses in the global macroeconomic outlook- have increased the urgency to find solutions to today's most pressing labor problems. This volume shows that, despite the still-low unemployment rates, the region may find itself trapped in a vicious cycle of poor-quality jobs -a phenomenon especially visible in the high percentage of informal jobs (which are defined in this publication as those without access to social security benefits) and in the high proportion of very short-lived jobs. As the title Jobs for Growth indicates, breaking this cycle will require comprehensive policies that boost productivity.

Civilian labor force data consists of the number of employed persons, the number of unemployed persons, an unemployment rate and the total count of both employed and unemployed persons (total civilian labor force). Labor force refers to an estimate of the number of persons, 16 years of age and older, classified as employed or unemployed. The civilian labor force, which is presented in these data tables, excludes the Armed Forces, i.e., the civilian labor force equals employed civilians plus the unemployed. Employed persons are those individuals, 16 years of age and older, who did any work at all during the survey week as paid employees, in their own business, profession or farm, or who worked 15 hours or more as unpaid workers in a family operated business. Also counted as employed are those persons who had jobs or businesses from which they were temporarily absent because of illness, bad weather, vacation, labor-management dispute, or personal reasons. Individuals are counted only once even though they may hold more than one job. Unemployed persons comprise all persons who did not work during the survey week but who made specific efforts to find a job within the previous four weeks and were available for work during the survey week (except for temporary illness). Also included as unemployed are those who did not work at all, were available for work, but were not actively seeking work because they were either waiting to be called back to a job from which they were laid off or waiting to report to a new job within 30 days. The unemployment rate represents the number of unemployed persons as a percent of the total civilian labor force.

Graph and download economic data for Civilian Labor Force in Southwest BEA Region (DISCONTINUED) (BEASWLF) from Jan 1990 to Dec 2015 about Southwest BEA Region, civilian, labor force, labor, and USA.

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 geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information

For the seventh edition (January 2025), the 2022 person weight (PWT22) was replaced with the 2024 person weight (PWT24). Only the person weight has been replaced with a 2024 version; the 2022 income weight (PIWT22) remains.

Workforce Management Market Outlook

As per our latest research, the global Workforce Management Market size reached USD 9.6 billion in 2024, reflecting robust demand across diverse industries. The market is expected to grow at a CAGR of 10.3% from 2025 to 2033, projecting a value of approximately USD 25.2 billion by 2033. This impressive growth trajectory is primarily driven by the increasing adoption of digital solutions for workforce optimization, the rising need for compliance with labor regulations, and the growing emphasis on productivity and cost-efficiency in organizations worldwide.

One of the most significant growth factors for the Workforce Management Market is the accelerating digital transformation across industries. Organizations are under increasing pressure to optimize their human resources, reduce operational costs, and enhance productivity. Workforce management solutions, encompassing time and attendance management, scheduling, and workforce analytics, enable companies to streamline labor processes, minimize errors, and ensure compliance with complex labor laws. The proliferation of cloud-based platforms and mobile applications has further democratized access to these tools, allowing businesses of all sizes to leverage advanced analytics and automation. This technological evolution is fostering a culture of data-driven decision-making, making workforce management solutions indispensable for modern enterprises.

Another key driver is the heightened focus on employee experience and engagement. As competition for talent intensifies, organizations are looking for ways to create flexible, responsive, and supportive work environments. Workforce management software provides real-time visibility into employee schedules, leave balances, and performance metrics, empowering both managers and employees to make informed decisions. The integration of artificial intelligence and machine learning into these platforms is enabling predictive analytics, personalized scheduling, and proactive management of workforce needs. This not only improves operational efficiency but also enhances job satisfaction and retention, which are critical for long-term business success.

Regulatory compliance is also a major catalyst for market growth. With labor laws and regulations becoming increasingly stringent and complex, especially in regions like North America and Europe, organizations are turning to workforce management solutions to avoid costly penalties and litigation. Automated compliance features, such as real-time tracking of work hours, overtime, and rest periods, ensure that companies adhere to legal requirements while also maintaining transparency and fairness in workforce administration. This compliance-driven demand is particularly pronounced in heavily regulated sectors such as healthcare, BFSI, and government, where accurate record-keeping and reporting are non-negotiable.

From a regional perspective, North America continues to dominate the Workforce Management Market, accounting for the largest revenue share in 2024 due to advanced technological infrastructure, high adoption rates of cloud solutions, and stringent labor regulations. Europe follows closely, driven by a strong focus on employee rights and digital workplace initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by rapid industrialization, expanding service sectors, and increasing investments in digital transformation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions recognize the value of workforce optimization in driving competitiveness and compliance.

Component Analysis

The Workforce Management Market by component is segmented into software and services, each playing a pivotal role in shaping the overall market dynamics. Software remains the cornerstone of workforce management solutions, offering a comprehensive suite of functionalities such as scheduling, time and attendance tracking, leave

Analysis of ‘Strategic Measure_Percentage Unemployment Rate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fb383985-5de9-4f55-ba17-581333f28ba9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains information about the unemployment rate in Austin (SD23 measure EOA.A.1). Texas Workforce Comission provides Texas Labor Market Information for Austin, the Austin Round-Rock MSA, Texas, and the United States.

This dataset includes the average number of people in the civilian labor force, the employment count, the unemployment count, and the unemployment rate for Austin, the Austin Round-Rock MSA, Texas, and the United States. The unemployment rate can be useful in understanding economic and workforce trends in Austin over time.

View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percentage-Unemployment-Rate/ehhu-nafn/

--- Original source retains full ownership of the source dataset ---

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) and governorates

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 is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households. Sample strata: The population was divided by: 1- Governorates 2- Type of Locality (urban, rural, refugee camps).

The estimated sample size is 7,616 households in each quarter of 2014, but in the second quarter 2014 only 7,541 households were collected, where 75 households couldn't be collected in Gaza Strip because of the Israeli aggression.

Mode of data collection

Face-to-face [f2f]

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 90.3% in 2014, and in quarters: First quarter 2014: 91.9% Second quarter 2014: 90.8% Third quarter 2014: 89.3% Fourth quarter 2014: 90.1%

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report

Contingent Labor Management Software Market size was valued at USD 3163.1 Million in 2024 and is projected to reach USD 7211.06 Million by 2031, growing at a CAGR of 10.85 % during the forecast period 2024-2031.

Global Contingent Labor Management Software Market Drivers

Growth of Contingent Workforce: Freelancers, contractors, and temporary employees are among the contingent labourers that the global workforce is coming to accept. Access to specialised talents, flexibility, and cost-effectiveness are some of the elements driving this trend. The need for software solutions to effectively manage this workforce is growing as companies depend more and more on freelance labour.

Requirement for Compliance and Risk Management: Across a range of businesses and geographical areas, regulations pertaining to contingent labour are getting stricter. Businesses want software that can guarantee adherence to tax rules, labour laws, and other legal requirements pertaining to independent contractors. These methods lessen the possibility of legal repercussions, worker rights abuses, and misclassification.

Goal for Cost Optimisation: By streamlining workforce management procedures, contingent labour management software helps businesses cut costs and improve operational effectiveness. Businesses may decrease administrative overhead and maximise resource allocation by automating processes like scheduling, payment processing, and onboarding.

Put an emphasis on talent management: The success of an organisation depends on attracting and retaining talent. Using the features that contingent labour management software offers, firms can find, evaluate, and manage contingent labour, attracting top talent and creating a flexible workforce that supports their strategic objectives.

Acceptance of Remote Work: The COVID-19 pandemic and technology improvements have sped up the transition to remote work, which has led to a greater reliance on contract employees who are able to work from anywhere. Organisations may efficiently manage distributed teams with the use of contingent labour management software, which makes remote collaboration, communication, and performance tracking possible.

Technological Developments: Constant technological developments in the fields of analytics, machine learning, and artificial intelligence are augmenting the functionalities of contingent labour management software. With the help of these technologies, managing contingent labour may be done more effectively and efficiently thanks to the automation of repetitive chores, intelligent sourcing recommendations, and predictive workforce analytics.

Demand for Scalable Solutions: Companies need contingent labour management software that can grow with them to meet their expanding workforce needs as they expand and adjust to shifting market conditions. With the flexibility and agility that scalable solutions provide, businesses can modify the size and makeup of their workforce in response to changing business needs.

While part-time employees constitute the primary workforce in the chain restaurant industry, their retention has become crucial in developed countries, especially Japan, due to labor shortages resulting from the declining birthrate and aging population. Analyzing employee reviews is an effective method for understanding factors that decrease employee satisfaction. However, while many analyses are focusing on full-time employees, there is insufficient analysis focusing on part-time employees, whose employment status and motivations differ from those of full-time employees. This study employs a Structural Topic Model to correlate latent topics from 4511 online text reviews with a 5-point scale of part-time employee satisfaction scores in Japanese chain restaurants. The study identifies 20 topics, including management systems and key employee interests. Especially digital communication and interview processes frequently appeared when satisfaction was low, which are unique to part-time employees in chain restaurants and had been overlooked in previous analyses. Further analysis links 20 topics to four 5-point scale HRM metrics (compensation satisfaction, workplace environment, motivation, and interpersonal relationships), enabling deeper analysis of the relationships between topics and HRM metrics. These insights contribute to the development of strategies to enhance part-time employee satisfaction in chain restaurants.