Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

This dataset provides information regarding the labour force, employment, unemployment, and other related labour market indicators, to facilitate research, policy-making, and public understanding of labour market conditions.

Econometric analysis on R of Italian and Sweden labour market dynamics, 2001-2021

Main labour market statistics time series data (large dataset).

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage.

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population but includes workers' hostels. However, persons living in private dwelling units within institutions are 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 a school compound would, however, be excluded.

Kind of data

Survey data

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

The GCC labour market size is expected to grow at a CAGR of 2.60% between 2025 and 2034. The market is being driven by increasing demand for labour to boost economic and industrial development, favourable labour laws in the region, and growing workforce.

Overview

This page presents a labour market analysis related to the Mayor of London’s priority sectors, as identified in the Local Skills Improvement Plan (LSIP). Each report (found below) explores a different economic sector in detail by bringing together data from a wide range of sources.

By offering insights into both current and historical trends, the analysis helps contextualise how each sector is performing, including relative to other industries and regions. These reports serve as a resource for strategic planning and policy-making in London.

An FAQ guide has been published alongside these reports to help users navigate their contents.

When using this analysis, please be aware of the following:

• The analysis is not intended to be fully comprehensive or exhaustive. It is a snapshot analysis of key data as it pertains to each sector in London.
• The data presented here reflects the information available at the time of publication. However, as the dataset relies on external sources, it may be subject to updates or revisions as new information becomes available or corrections are made by the original data providers. Users are encouraged to verify the data periodically for the latest version.
• Due to limitations in data collection methods or reporting frameworks, some aspects of the dataset may reflect biases or gaps inherent to the original data sources.
• The analysis does not represent the full body of evidence on which Mayoral Policies are, or will be, based.

Outputs should be triangulated with other sources of information and analysis to develop a more rounded statistical and contextualised picture of any specific policy issues.

This page presents a labour market analysis related to each of the Mayor of London’s priority sectors. Each report explores a different economic sector in detail by bringing together data from a wide-range of sources. An FAQ guide has also been published alongside these reports to help users navigate their contents. When using outputs from this analysis it is important to be aware of the following caveats: The analysis is not intended to be fully comprehensive or exhaustive. It is a snapshot analysis of key data as it pertains to each sector in London. The data presented here reflects the information available at the time of publication. However, as the dataset relies on external sources, it may be subject to updates or revisions as new information becomes available or corrections are made by the original data providers. Users are encouraged to verify the data periodically for the latest version. Due to limitations in data collection methods or reporting frameworks, some aspects of the dataset may reflect biases or gaps inherent to the original data sources. The analysis does not represent the full body of evidence on which Mayoral Policies are, or will be, based. Outputs should be triangulated with other sources of information and analysis to develop a more rounded statistical and contextualised picture of any specific policy issues.

Analysis of the labour market covering the latest developments. When using outputs from this analysis you should be aware of the following caveats: •_The analysis is not intended to be comprehensive or exhaustive. It is a snapshot analysis of key data as it pertains to London._ •_The analysis does not represent the full body of evidence on which Mayoral Policies are, or will be, based._ We advise that our outputs are triangulated with other sources of information and analysis to develop a rounded statistical picture of any specific policy issues.

Monitoring and assessing the Employment Insurance (EI) program helps provide a clear understanding of its impact on the Canadian economy and its effectiveness in addressing the needs of Canadian workers, their families and their employers. These files include data from Annex 1 Key Labour Market Statistics Data Tables.

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

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 33 000 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

Face-to-Face and Computer Assisted Personal and Telephone Interview

The Research Data Centre at the Institute for Employment Research (RDC-IAB) has been offering high-quality administrative and survey data on the German labour market for 15 years and has become one of the most important locations worldwide for researchers interested in data for labour market research. This article provides an overview of the RDC-IAB, including its data and access modes. The article presents two datasets in more detail: the SIAB, a classic dataset, and the LPP, a new dataset. Finally, this article provides insights into future infrastructure and data developments.

An essential resource for all users of UK economic and labour market statistics. It draws together the expert research and analysis and range of content found in Economic Trends and Labour Market Trends to build an up-to-date, comprehensive and unique statistical picture of the UK economy and labour market. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: ELMR

Labour market indicators for UK countries and regions, including employment, unemployment and economic inactivity, rolling three-monthly figures published monthly, seasonally adjusted. Labour Force Survey.

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 LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

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. However, volumes are updated periodically by ONS, 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.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

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: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

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

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

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

Statistics on the labour market outcomes of postsecondary graduates, including the employment status and estimated gross annual earnings, are presented by province of residence at interview, the level of study, the field of study, sex and work-integrated learning (WIL) participation.

Labour market outcomes of postsecondary graduates, employment status, annual earnings, work placements, by province of study, level and field of study and gender.

Labour market indicators for UK constituent countries and English regions, including employment, unemployment, economic inactivity, workers' hours, jobs and Claimant Count, published monthly.