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.

Data and analysis of Washington's employment conditions, economy, job market and workforce. These reports can help make informed career, hiring and policy decisions.

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.

Discover the "Job.com USA Jobs Dataset," a detailed resource that provides an in-depth look at the job market in the United States.

This dataset is sourced from Job.com, a leading employment platform in the USA, and includes comprehensive information on job listings across various industries and regions.

Key Features:

• Extensive Job Listings: Features a wide range of job postings from different sectors and industries, offering a comprehensive overview of employment opportunities across the United States.
• Detailed Information: Each listing includes important details such as job titles, company names, job descriptions, locations, employment types (full-time, part-time, remote, contract), required qualifications, and salary data.
• Insights into Market Trends: Analyze current trends in the US job market, including in-demand skills, leading employers, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research and Analysis: This dataset is perfect for researchers, HR professionals, and data analysts interested in studying labor market trends, developing recruitment strategies, or understanding the employment dynamics in the USA.

The Job.com USA Jobs Dataset offers valuable insights into the American job market, making it a crucial resource for job seekers, employers, and researchers alike. Use this dataset to stay ahead of market trends, explore employment opportunities, and gain a deeper understanding of job market dynamics in the United States.

The global market size of Labor is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Labor Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Labor industry. The key insights of the report:
1.The report provides key statistics on the market status of the Labor manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Labor industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Labor Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Labor as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Labor market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

The global Labor Market Intelligence Platform market is projected to grow from USD 3,455.3 million in 2025 to USD 10,842.8 million by 2033, exhibiting a CAGR of 16.5% from 2025 to 2033. The market growth is primarily driven by the increasing demand for data-driven insights into labor market trends, skills gaps, and talent availability. The rising adoption of cloud-based platforms, the growing need for talent management and workforce planning, and government initiatives to enhance labor market efficiency are further contributing to market expansion. North America is expected to dominate the Labor Market Intelligence Platform market throughout the forecast period, owing to the presence of well-established vendors, early adoption of technology, and a large pool of skilled professionals. Asia Pacific is anticipated to witness significant growth in the coming years, driven by the increasing demand for talent in emerging economies and government initiatives to improve labor market information systems. The key players in the market include LinkedIn, Lightcast, Claro Analytics, Coresignal, Horsefly, Chmura, Talismatic, Textkernel, WageScape, Liepin, BOSS Zhipin, 51Job, and Zhilian Zhaopin. These companies offer a range of solutions to meet the diverse needs of organizations, including data-driven insights, talent management tools, and recruitment solutions.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

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. ...

The Labor Market Intelligence Platform market is rapidly evolving, driven by the increasing demand for accurate workforce analytics and insights that can guide strategic decision-making for organizations. These platforms aggregate, analyze, and present data related to employment trends, skill requirements, and labor

Note: Updates to this data product are discontinued. County boundaries do not always accurately define local economies. Commuting zones and Labor Market Areas combine counties into units intended to more closely reflect the geographic interrelationships between employers and labor supply.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data download page For complete information, please visit https://data.gov.

US Tariff Impact on the Market

The impact of US tariffs on the digital labor market is significant, particularly due to their potential to disrupt supply chains, cost structures, and international trade relationships. With the digital labor market heavily reliant on global outsourcing and technology platforms, the imposition of tariffs could lead to higher operational costs for businesses operating across borders.

Specific sectors, such as customer support and online platforms, may face a 3-5% increase in expenses due to tariffs, impacting pricing strategies and profitability. Additionally, US-based companies that rely on foreign labor could be forced to either absorb the costs or pass them on to consumers, leading to a potential decline in competitiveness.

On the other hand, tariffs could incentivize the relocation of some services back to the U.S., creating more localized digital labor opportunities, albeit at a higher cost. This dynamic may reshape market structures, requiring companies to innovate in response to changing cost pressures.

➤ Get a sample copy to discover how our research uncovers business opportunities here @ https://market.us/report/digital-labor-market/free-sample/

Abstract

The Egypt Labor Market Panel Surveys (ELMPSs) of 1998, 2006, and 2012 and Jordan Labor Market Panel Survey (JLMPS) of 2010 have become well-recognized data sources for labor market studies in the Middle East and North Africa (MENA). These two surveys have been used in numerous research endeavors including peer reviewed academic publications, dissertations, and international organization reports. As part of the same series of surveys, the Tunisia Labor Market Panel Survey (TLMPS) of 2014 is the first wave of what will eventually become a longitudinal survey of the Tunisian labor market. Being far richer than any currently available data, the TLMPS 2014 is a much-needed addition in a landscape of otherwise scarce publicly-accessible data on the Tunisian labor market. The TLMPS 2014 was collected in partnership between the Economic Research Forum (ERF) and the Tunisian National Institute of Statistics (INS).

Similarly to its Egyptian and Jordanian counterparts, the TLMPS 2014 is a nationally representative survey that features detailed information on households and individuals, especially in regards to labor market characteristics. As in other countries in the MENA region, Tunisia suffers from high unemployment, particularly for university graduates, youth, and women, and from low female labor force participation.

The survey allows for an in-depth investigation of current employment characteristics as well as analyses of broader labor market dynamics. For instance, analyses have already revealed the particularly long unemployment durations Tunisian youth experience, long even in comparison to other countries in the region.

For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).

Geographic coverage

The sample covered urban/rural areas of each of Tunisia's governorates

Analysis unit

1- Households. 2- Individuals.

Universe

The survey covered a national sample of households and all households members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample frame included around 5,160 households drawn from a larger sample that is regularly used to conduct the quarterly survey on population and employment in Tunisia. This larger sample contained 18,000 households as of the last quarter of 2012. The drawing of the sample was done in two stages. In the first stage, 258 enumeration areas were randomly drawn according to the principle of probability proportional to size from the list of enumeration areas drawn up in the 2004 Census. This first sampling stage was carried out using 46 strata comprised of the urban/rural components of each of Tunisia's governorates. The final sample was made up of 253 clusters (out of a possible 40,377 nationally). In the second stage, 20 households were supposed to be drawn at random from each cluster. This procedure was, however, not strictly followed in the field.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey incorporates questionnaires to be administered at both the household and individual levels. At the household level, there was a general household questionnaire, as well as a questionnaire specifically about current migration, transfers, and agricultural and non-agricultural enterprises. At the individual level, there was a detailed questionnaire for working age individuals (15+) and an abbreviated version of the questionnaire for those 6-14 years old.

The main household questionnaire and the migration/enterprise questionnaire were designed to be answered by the most knowledgeable individual in the household, usually the head or the spouse of the head. Along with information on the characteristics of the dwelling, access to public services, and ownership of durables, the household questionnaire includes a full household roster with information on basic demographic characteristics, such as age, sex, and relationship to the head of household. The migration/enterprise questionnaire includes information on any family members currently abroad, remittances, and other transfers, such as child support and pensions. Data were gathered on both non-agricultural and agricultural enterprises, including assets used and net revenues.

The ELMPS and JLMPS had a single questionnaire for all individuals regardless of age. However, in Tunisia, a distinct questionnaire for individuals 6-14 was designed in order to more carefully incorporate measures of child labor. As very little child labor was detected even with this special design, in future LMPSs we plan to revert to a single questionnaire with a few additional questions targeted to children 6-14.

The questionnaire includes a variety of modules on labor market experience and outcomes and related issues. On the labor market side, it elicits information on the current labor market status of the individual, detailed job characteristics (for the employed), wage earnings and non-wage benefits (for wage workers) and participation in domestic and subsistence work. Those who work were asked about both primary and secondary jobs (if any). The questionnaire also includes a detailed labor market history starting from the first labor market status after leaving school and moving forward towards the present for those who ever worked. Further, there is a detailed section on return migration for those who ever worked abroad.

The labor market intersects with a number of other important life experiences, such as education, fertility, and marriage, which are also captured in the TLMPS individual questionnaire. For instance, there are modules on family background (parents and siblings), educational experiences, health, and residential mobility. For women, a section is devoted to fertility issues, the status of women in the household, and work-family issues such as child care and maternity leave. Data were also collected from both men and women on marriage and decisions around marriage, such as the incidence of kin marriage and living arrangements at marriage. Finally, there are modules on financial decision-making, with specific questions about savings and borrowing, as well as on the use of information technology.

Response rate

There were several different problems with non-response during the fielding. First, households often refused to respond entirely. Second, in completing the household survey, some individuals were not captured and some households refused or failed to answer the migration/enterprise questionnaire. In this section we discuss the patterns of non-response, which are incorporated into the weights, discussed below:

1. Non-response of the entire household While the initial goal was to collect data from 5,160 households, time pressures reduced the intended sample to 4,986 households. Of the 4,986 households initially selected, interviews were completed with only 4,521, generating an overall household non-response rate of 9.3%. Additionally, because several clusters were found not to have the requisite twenty households at the end of the data collection stage, additional households were added to some clusters to improve the response rate, leading to wide variation in the number of the households per cluster. The minimum number of households interviewed in a cluster was 8 and the maximum was 34. The mean was 19.7, and the median was 20, with the interquartile range going from 17 to 22 households.

After this additional work to add households to the sample, non-response rates at a cluster level ranged from 0% (complete response), which occurred for 29% of clusters, to a maximum of 62.5%. The mean non-response at the cluster level was 10.2%, the median was 6.7%, the 75th percentile was 13.3%, and the 90th percentile was 24.8%. This household non-response is incorporated in the weights at a cluster level, with the households that did respond within a cluster representing those that did not.

1. Non-response to child, adult, and migration/enterprise questionnaires As well as problems with non-response on the household level, there were problems with completing the child, adult, and migration/enterprise questionnaires. We developed weights to account for non-response to each of these questionnaires in their entirety. However, individuals often stopped answering partway through a questionnaire, suffered from incorrect skips, or other data problems, such that data is sometimes missing for a particular question within a questionnaire that contains some data. Additional data imputation techniques, implemented on a question-by-question basis, are required for these problems.

China City Labor Market: Demand: Sci Research, Polytech Ser & Geo Prospect data was reported at 37.943 Person th in Mar 2014. This records an increase from the previous number of 36.768 Person th for Dec 2013. China City Labor Market: Demand: Sci Research, Polytech Ser & Geo Prospect data is updated quarterly, averaging 42.023 Person th from Sep 2005 (Median) to Mar 2014, with 35 observations. The data reached an all-time high of 65.382 Person th in Sep 2007 and a record low of 21.324 Person th in Mar 2006. China City Labor Market: Demand: Sci Research, Polytech Ser & Geo Prospect 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 of Labour: by Industry.

Temporary Labor Market size is projected to reach over USD 794.35 bn by 2032 from a value of USD 556.04 bn in 2024 and is projected to grow by USD 571.49 bn in 2025, registering a CAGR of 4.6% from 2025 to 2032.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

Digital Labor Market is estimated to reach USD 23.7 Billion By 2034, Riding on a Strong 17.20% CAGR throughout the forecast period.

Stay updated with Market Research Intellect's report_name, valued at current_value in 2024, projected to reach forecast_value by 2033 with a CAGR of cagr_value (2026-2033).

Abstract

The Sudan Labor Market Panel Survey 2022 (SLMPS 2022) is the first wave of a planned longitudinal study of the Sudanese labor market designed to elucidate the way in which human resources are developed and deployed in the Sudanese economy. The SLMPS 2022 is a nationally-representative household survey on a panel of about 5,000 households planned to be repeated every six years. The focus of the survey is to understand key relationships between labor market processes and outcomes and other socio-economic processes such as education, training, family formation and fertility, internal and international migration, gender equality and women's empowerment, enterprise development, housing acquisition, and equality of opportunity and intergenerational mobility.

The SLMPS 2022 is modeled on similar surveys carried out in Egypt in 1998, 2006, 2012, and 2018 in Jordan in 2010 and 2016, and in Tunisia in 2014. All of these surveys started out with a sample of 5,000 households in the first wave and then the sample grew as a results of household splits and the addition of a refresher sample in every new wave. The SLMPS 2022 also includes modules from the Living Standards Measurement Study Plus (LSMS+) surveys that focus on gender disaggregated asset, employment, and entrepreneurship data. Given the level of detail desired in the individual level information, it is crucial in this survey that the information be collected from the individual him or herself rather than from any informant in the household. Therefore, the survey design calls for a number of visits to the same household to make sure that each individual aged five and older can be interviewed in person.

===============================================================================================

For details on the key characteristics of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Geographic coverage

The sample was designed to provide estimates of the indicators at the national level, for urban and rural areas, and for all regions.

For detailed information on the regions and governorates used in the SLMPS 2022 Sample, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Analysis unit

1- Households. 2- Individuals. 3- Household Enterprises.

Universe

The survey covered a national sample of households and all household's members aged five and above. In addition, the survey covered enterprises operated by the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A fundamental challenge when designing the SLMPS sample was the lack of a recent, nationally representative sample frame. The last national population census in Sudan was in 2008, before the secession of South Sudan. There had also been limited updating of administrative borders and maps. The first level of administrative geography in Sudan is the state (wilaya), and there are 18 states in Sudan. The second level of administrative geography in Sudan is the locality (mahaliya), and CBS had updated the borders of localities in 2017 to 189 distinct geographies (each locality nested within a single state).). The principal investigators (C. Krafft and R. Assaad) used the updated borders combined with 2020 population estimates based on remote sensing data to create our sampling frame and draw our sample. These sources were supplemented with additional data to identify refugee and IDP camps and areas for our strata. The planned sample design was a random stratified cluster sample made up of 5,000 households sub-divided into 250 primary sampling units (PSUs). The strata represented in the sample are: (i) refugee camps, (ii) refugee areas (areas with non-camp refugee settlements), (iii) IDP camps, (iv) IDP areas (areas with non-camp IDP settlements), (v) other (non-refugee/non-IDP) rural areas,

(vi) other urban areas.

For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Sampling deviation

The realities of the sample frame and the logistics of fielding led to a number of deviations from the planned sample in fielding. While the initial sample was estimated to have a reasonable number of households in each PSU based on satellite imaging and population projections, there were cases where a PSU did not, in fact, have any or many households. All PSU locations were reviewed first in the CBS offices to identify locations that were empty or where there appeared to be five or fewer households and these locations were replaced with backup PSUs. There were a variety of reasons why a PSU might have few or no households, including that it consisted of industrial/commercial (not residential) buildings, that it was a mine or grain storage area, or that it had rocks or grain silos that looked like residences. When office review determined there were at least five or more potential households on the satellite maps, fielding was attempted. However, a number of issues arose in the field as well. Upon visiting, buildings were determined to be non-residential, or were abandoned. Furthermore, a number of locations were determined to be unsafe to field, a status that even changed and fluctuated frequently during the fieldwork. Persistent sandstorms also prevented fielding in specific localities. The rainy season likewise made some locations inaccessible for fielding. Backup samples were created; initially one urban and one rural backup were provided per state, and further backups were drawn as needed to replace PSUs that could not be fielded. Backups were, if possible, from the same strata and always from the same state. When possible, additional backups were also drawn from the same locality in an attempt to minimize bias. However, there were cases when an entire locality became inaccessible. Ultimately, 152 PSUs from the original sample of 250 were fielded in the initially planned locations. Nine of the initially planned backups were used. For the remainder, 24 were replaced by the first replacement given, 17 by the second, 17 by the third, 9 by the fourth, 6 by the fifth, 4 by the seventh, and the remaining 12 by various higher order replacements. Repeated replacements tended to occur in localities with a high share of buildings (e.g. mines, grain storage) that the population estimates likely mistook for residences.

===============================================================================================

For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Mode of data collection

Face-to-face [f2f]

Research instrument

The SLMPS questionnaires consist of a household questionnaire and an individual questionnaire, with modules. The modules built on and ensured substantial comparability with other LMPSs. The household questionnaire includes: (i) identifiers and household location (ii) roster of household members (iii) housing conditions and durable assets (iv) current household member migrants abroad (v) remittances (vi) other income and transfers (vii) shocks and coping mechanisms (viii) non-agricultural enterprises, including information on characteristics, employment of household members and others, assets, expenditures, and revenue (ix) agricultural assets, land and parcels, capital equipment, livestock, crops, and other agricultural income. The individual questionnaire collects data from all individuals 5 and older (children under five are captured in the household roster). The individual questionnaire elicits information about (i) residential mobility (ii) father's, mother's and sibling characteristics (including siblings abroad) (iv) health (v) education level and detailed educational history (vi) training experiences (vii) skills (viii) current employment and unemployment (viii) job characteristics for the primary and secondary job (ix) labor market history (x) costs and characteristics of marriage (ix) fertility (xii) women's employment (xiii) wages from primary and any secondary jobs (xiv) return migration, refugee, and IDP experiences for Sudanese respondents (xv) modules for immigration and refugees for non-Sudanese respondents (xvi) information technology (xvi) savings and borrowing (xvii) attitudes (xviii) time use (a full 24 hour diary for adults and a shorter module for children) and (xix) a series of questions on rights to parcels, livestock, and durables.

For more details, see the questionnaires in the documentation.

Response

Artificial Intelligence (AI) is rapidly transforming the labor market, reshaping how industries operate and how work is performed. As a driving force behind automation, AI enhances productivity, reduces operational costs, and improves decision-making processes across various sectors-including healthcare, manufacturi

Techsalerator’s Job Openings Data in Africa offers a comprehensive and insightful dataset designed to provide businesses, recruiters, labor market analysts, and job seekers with a thorough view of employment opportunities across the African continent. This dataset aggregates job postings from a wide range of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Africa.

Key Features of the Dataset: Broad Coverage:

The dataset aggregates job postings from numerous sources including company career pages, job boards, recruitment agencies, and professional networking sites. This extensive coverage ensures a broad spectrum of job opportunities from multiple channels. Daily Updates:

Job posting data is updated daily, providing real-time insights into the job market. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This categorization allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

The dataset includes detailed information on job openings across different countries and regions within Africa. This regional breakdown helps users understand job market dynamics and opportunities in various geographic locations. Role and Skill Insights:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in finding opportunities that match their expertise and helps recruiters identify candidates with the desired skill sets. Company Information:

Users can access details about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where the demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. African Countries Covered: Northern Africa: Algeria Egypt Libya Mauritania Morocco Sudan Tunisia Sub-Saharan Africa: West Africa: Benin Burkina Faso Cape Verde Ivory Coast (Côte d'Ivoire) Gambia Ghana Guinea Guinea-Bissau Liberia Mali Niger Nigeria Senegal Sierra Leone Togo Central Africa: Angola Cameroon Central African Republic Chad Congo, Republic of the Congo, Democratic Republic of the Equatorial Guinea Gabon São Tomé and Príncipe East Africa: Burundi Comoros Djibouti Eritrea Eswatini (Swaziland) Ethiopia Kenya Lesotho Malawi Mauritius Rwanda Seychelles Somalia Tanzania Uganda Southern Africa: Botswana Lesotho Namibia South Africa Swaziland (Eswatini) Zimbabwe Benefits of the Dataset: Enhanced Recruitment Strategies: Recruiters and HR professionals can use the dataset to identify hiring trends, understand competitive practices, and refine recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, making their job search more efficient and targeted. Strategic Workforce Planning: Companies can gain valuable insights into the availability of talent across Africa, assisting with decisions related to market expansion, office locations, and talent acquisition. Techsalerator’s Job Openings Data in Africa is a critical resource for understanding the diverse and evolving job markets across the continent. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.