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.

In 2021, around ********** employees in China were placed by private employment agencies. In comparison, the number of agency-conducted labor market placements in Australia amounted to approximately half a million workers that year.

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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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)

According to a December 2022 report, the financial technology and technology industries saw the highest increases in job cuts when compared with the previous year. The financial technology (FinTech) industry saw a ******* percent increase in job cuts in 2022. FinTech companies are those using non-traditional financial methods to deliver financial services such as AI, blockchain, cloud computing, and big data. The FinTech industry saw boom during the early days of the pandemic, driven by low interest rates and tight financial conditions for consumers.

A table showing the Jordanian labor market indicators for the year 2021

In 2021, approximately 28.3 million employees in the Asia-Pacific region were placed by private employment agencies. This marked an increase from around 15.6 million people placed by private agencies in the region in 2017.

Office for National Statistics' national and subnational Census 2021. OccupationThis dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by occupation. The estimates are as at Census Day, 21 March 2021. Occupation (current) definition: Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels.Quality information: As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes.Comparability with 2011: Not comparableWe changed the classification for Census 2021 and combined the categories previously available in the 2011 Census data. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Current labour market data for Austria. These include data on employment, unemployment and labour supply

The data covers job search activities and employment outcomes for participants in an online study on the provision of occupational recommendations to job seekers. Providing job search assistance to job seekers in a cost effective manner is a challenging goal. Interventions aimed at providing tailored advice typically involve large personnel costs that often dissipate the benefits. However, the advances in information technologies and the shift of formal job search to online platforms over the last 20 years offer new opportunities for providing advice at very low-cost. In this study we examine the potential for providing on-line advice to a population of hard-to-place job seekers. In a randomized field experiment, we provided suggestions about suitable alternative occupations to long-term unemployed job seekers. The suggestions were automatically generated, integrated in an online job search platform, and fed into actual search queries. Effects on the primary pre-registered outcomes of "finding a stable job" and "reaching a cumulative earnings threshold" are positive, large, and are more pronounced for those who are longer unemployed. Treated individuals include more occupations in their search and find more jobs in recommended occupations.The crisis and its aftermath have thrown up many challenges for macroeconomics. For the past thirty years the predominant methodology in macroeconomics has been a class of models that assume an absence of heterogeneity across firms, individuals, etc., and assume that individuals have access to well-functioning insurance markets. These models have been widely criticised for providing no insight into the current crisis. The crisis has highlighted i) the extreme nature of labour market responses as unemployment has remained high while nominal wages have remained inflexible; ii) the importance of credit markets in generating as well as propagating shocks.. It is our view that that a deeper understanding of credit and labour markets, how they interact and how shocks in these markets aggregate and propagate is fundamental to the understanding of the macroeconomy. This agenda requires building a model of the economy based on realistic features of credit and labour markets including differences in information among agents, differences in attitudes towards risk, the inability to specify or contract upon all future contingencies, and recognising the limits of contractual enforceability. It requires an understanding of how behaviour in individual markets aggregates and how, in turn, the macroeconomic environment feeds back to individual markets. Our aim is to transform research in macroeconomics and to build its foundations on a thorough understanding of credit and labour markets. Credit markets: We will consider why financial markets occasionally dry up, why banks simultaneously borrow and lend to each other and how this affects financial risk and monetary policy. An important component of this analysis is that differences in information between holders of assets and potential buyers creates illiquidity, that is, holders find it costly to reverse an asset trade once made. The relationship between contagious illiquidity and market failure, such as we have seen in the financial crisis, is a core element of this theme. Labour markets: Traditional models have difficulties accounting for the fluctuations, and the sluggishness in responses, of employment and wages. We will investigate this issue from two angles. First, we will look into the black-box of standard job search models by examining how job-seekers determine which jobs to apply for, how this changes with unemployment and how selections depend on occupation, salary and travel distance. Second, we will examine the nature of the employment relationship after job search is completed, its durability, the evolution of wages and productivity and the dependence of both on current, past, and anticipated macroeconomic conditions. The macroeconomy: An economy is the aggregation of the activities in individual markets. It is important to know if behaviour at the level of individual markets is amplified or washed-out at the aggregate level. For example, if employment responses at the firm level are sluggish, does this imply sluggish responses at the macro level? Understanding this aggregation issue requires insight into the structure of employment responses at the firm level. We expect that the joint analysis of credit and labour markets and how they aggregate will provide new insights for the understanding of the macroeconomy. The data was collected in collaboration with a private provider of job search assistance programs for long-term unemployed in the UK. The provider hosts an online job search portal. Our study selected all job seekers that registered on the portal between 2019-01-01 and 2020-10-01 and that consented to participation in the study. For these participants, all job search data was collected through a set of API's that received the information directly from user activity on the portal. Information on job finding was provided by the provider of job search assistance programs that ran the online job search portal.

The COVID-19 pandemic, which first hit Italy in late February 2020, had massive repercussions on life in the country. The virus measures adopted by the Italian government to control the contagion pushed many businesses to stop or radically scale down their activities. According to a recent survey conducted among Italian workers, opinions about the impact of accelerating technological change on the job market were more or less equally distributed. About ** percent of the respondents, in fact, believed technology will have a positive impact on the job market. However, when taking into account only workers with a middle school diploma as their highest educational achievement, the share of respondents with a negative view on the topic increased to roughly ** percent.

City Labor Market: Demand-Supply Ratio: Wuhan data was reported at 1.790 NA in Sep 2021. This records a decrease from the previous number of 1.930 NA for Jun 2021. City Labor Market: Demand-Supply Ratio: Wuhan data is updated quarterly, averaging 1.150 NA from Mar 2001 (Median) to Sep 2021, with 56 observations. The data reached an all-time high of 2.010 NA in Mar 2021 and a record low of 0.600 NA in Dec 2001. City Labor Market: Demand-Supply Ratio: Wuhan data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand-Supply Ratio.

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.

Employment status on Census Day 2021, by personal characteristics.

This table contains quarterly and yearly figures on labour participation in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status, and the average working hours. A division by sex, age and level of education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of May 27, 2025: The figures for the 1st quarter 2025 have been added.

Changes as of August 23, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in August 2025.

The contents of the dataset are related to the employment and unemployment of foreign workers in the province of Trento. The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to 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-workers-foreigners/Year-2021 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. Data presented in absolute values shall be rounded to the nearest hundred. For this reason, the total may not correspond to the sum of the individual values. The indent ”-“ replaces the unpublished data as not available or not determinable or not publishable 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 Labour Market and Policy Studies Office on Labour Service – PAT, Labour Agency (Employment Centres) – PAT data and ISPAT data.

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.