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

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

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

Labor Force Participation Rate in the United States increased to 62.40 percent in September from 62.30 percent in August of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August 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.

Employment Rate in the United States increased to 59.70 percent in September from 59.60 percent in August of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Provide the annual business report data of the Department of Labor.

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 Department of Economic Research Data Index provides information about the economy and labor market in Massachusetts. It includes links to data tools, analytic reports, data visualizations, dashboards and other resources in the following areas:

• Unemployment and Labor Force Data
• Employment Information by Industry
• Employment Information by Occupation
• Information on Massachusetts Employers
• Location Data

Employment by age and sex for UK regions and countries, rolling three-monthly figures published monthly, not seasonally adjusted. Labour Force Survey.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

The Temporary Labor Market size was valued at USD 491.52 billion in 2023 and is projected to reach USD 758.81 billion by 2032, exhibiting a CAGR of 6.4 % during the forecasts period.

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.

Graph and download economic data for Total Unemployed, Plus All Persons Marginally Attached to the Labor Force, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Persons Marginally Attached to the Labor Force (U-6) (U6RATE) from Jan 1994 to Sep 2025 about marginally attached, part-time, labor underutilization, workers, 16 years +, labor, household survey, unemployment, and USA.

Abstract

The Egypt Labor Market Panel Survey 2006 (ELMPS06) is the first full-fledged panel study of its scale in Egypt. This panel follows a nationally representative sample of 4,816 households visited in 1998, households that split from that sample, plus a refresher sample of 2,500 households. The total number of households reached in 2006 is 8,349. The ELMPS06 provides estimates of employment, unemployment and underemployment. The survey also collects information on job characteristics, mobility, and earnings. Collected data covers issues of household socio-economic characteristics, demographic characteristics, family enterprises and women’s status and work. A separate community level questionnaire has been administered to collect data on access to services and work opportunities in sampled localities. This report provides information on the different methodological issues related to the survey including sampling, questionnaire design, training of field staff, data collection, office review, and data entry.

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.

Analysis unit

1- Households. 2- Individuals.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ELMPS06 sample consists of three types of households: 1. Households visited in 1998 2. Split households 3. A refresher sample of 2,500 households In this section we describe in details the sampling techniques for both the 1998 and 2006 samples. We also describe the attrition to the 1998 sample due to loss of some household identification data, which were kept by CAPMAS.

The Selection Process of the 2006 New Sample This sample was selected from the CAPMAS 2005 Master Sample. This is a nationally representative two-stage self-weighted (to the extent possible) sample. Each governorate is allocated a number of PSUs in the master sample that is proportionate to its size and its urban/rural distribution.

The master sample was prepared through a two-stage process. First, shiekha's and villages are selected by probability proportionate-to-size method from two different sampling frames (one urban and another rural). In the second stage, these selected primary sample units are divided into secondary sampling units of 700 households each. A total of 1200 sampling units are then randomly selected to constitute the final master sample of CAPMAS. The ELMPS06 2006 new sample was proportionately selected from the CAPMAS master sample. Primary sampling units were then randomly selected from the CAPMAS master sample. Then within each PSU (containing 700 households in the master sample) we randomly selected 25 households. The 5,000 households that constitute the initial survey sample in 1998 were selected from a CAPMAS master sample prepared in 1995. The master sample consists of 750,000 households in 500 primary sampling units (PSUs) each consisting of 1,500 households. The CAPMAS master sample was selected through a two-stage process. The country is first divided into two strata: urban and rural. Each stratum is in turn divided into sub-strata representing each governorate. All the villages (in the case of rural strata) or shiyakhas (urban quarter, in the case of urban strata) in each substratum were listed and assigned a weight based on their population. The first stage consisted of choosing the villages and shiyakhas that would be represented in the sample based on the principal of probability proportional to size. This meant that a shiyakha or a village is possibly selected more than once if its size warrants that. The selected shiyakhas and villages are then divided into PSUs of approximately 1500 housing units each; then one or more PSUs are selected from each shiyakha or village. The selected PSUs were then re-listed in 1995 to enumerate all the households selected. As shown in Table 6, the master sample contains 306 urban PSUs and 194 rural PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire design was finalized by the ERF team prior to the conclusion of the agreement between CAPMAS and ERF. The household-level research instruments comprise of three interrelated questionnaires for each household. The household questionnaire collects data on the different demographic characteristics of household members, household assets and access to services. This questionnaire also includes a module that tracks individuals who were part of the 1998 sample. The questionnaire allows space for 20 individuals as members of the household and for 10 splits. The individual questionnaire includes modules on the education and work characteristics for individuals six years and up. The printed version allows space for only five individuals, but more than one individual questionnaire can be used for a household depending on its size. The third questionnaire in the household-level research instrument is the “Migration, Family Enterprises and Non-wage Income” questionnaire, which includes the modules on migration, remittances, non-work-related income sources, and non-agricultural household enterprises.

----> Additions and changes to the 1998 Questionnaire

New sections were added to the 1998 questionnaires and a number of questions were deleted because they did not produce useful results after the analysis. The following are the major changes to the 1998 questionnaire: 1. The panel design mandated a number of changes, including the addition of a new section, Section 0.2, which gathered information on the basic characteristics of members who lived in household in1998 but no longer live in household in 2006 and their new addresses to track them. The cover page also included a question regarding the type of the household (whether it is originally visited in 1998, is a split household, or from the new sample). Section 0.1, the household roster, also included an additional question (0105), which inquires about the individual’s person number (pn) in the 1998 data set. Data collectors were able to get this information from the data sheets that were printed for each household containing basic demographic characteristics and a summary of her/his work and education characteristics. 2. Questions about land ownership and cultivation were added in section 0.3. Although they do not quite fit under housing and services, this was the best place to include them. Instructions during training were to write zero if no land was owned or rented by household. 3. The section on durable goods, section 0.4, now includes questions on whether the item was bought at the time of marriage and whether an item is bought to be used by a household member after she/he marries. 4. A short section on siblings (section 1.3) was added, which refers to total number of siblings, and whether or not they reside in the same household. 5. The section on education is expanded significantly. It now includes questions about the characteristics of secondary, preparatory, and primary schools, where relevant. Questions about repetitions and interruptions of schooling are included in order to gain better understanding of the number of years of schooling as opposed to grade level achieved and age of exit/completion. The section also allows one to assign a unique code for each school attended by the individual. These unique codes were received from the Ministry of Education and allow for analysis on school characteristics based on further data from the Ministry. 6. The migration section was moved earlier in the questionnaire so that it applies to all individuals whether they worked or not. In 1998, this section only applied to those who had previously worked. The section now applies to all those aged 15 and above. It also includes a new question about place of birth. 7. In the sections on work characteristics, we no longer have a reference week and a reference three months. We used instead the past seven days (counting back from day of first interview with individual) and the past three months. 8. In the unemployment section (Section 4.2), we added questions about the use of a landline or cell phones in job search activities. We also separated the question on registering with a government agency from the job search question. Now all the activities listed under job search are limited to the past three months reference period. 9. We separated the questions on subsistence and domestic work in a new section. These questions now apply to all children aged 6-17 and all women aged18-64, irrespective of employment status. The questions on domestic work are now much more detailed than before and ask about time spent on various domestic chores during the past 7 days. If the same amount of time is spent everyday, then interviewers were instructed to multiply the daily times by seven. However, this is designed to allow for variations in schedules every day. One of the reasons this section now applies to the past week rather than a reference week was that that it might be difficult to get an accurate estimate due to recall problems. Only the last question of the section allows for the activity to be done concurrently with other activities (child care). Otherwise, interviewers were instructed that they are enquiring about the time spent exclusively on the activity in question. 10. Questions about the “first job” were added into the section detecting employment in the forgoing three months. As in the job mobility section, to qualify as a job, the individual must have spent at least 6 months at the job. Thus, a job

Job Dataset

This dataset provides a comprehensive collection of synthetic job postings to facilitate research and analysis in the field of job market trends, natural language processing (NLP), and machine learning. Created for educational and research purposes, this dataset offers a diverse set of job listings across various industries and job types.

Descriptions for each of the columns in the dataset:

1. Job Id: A unique identifier for each job posting.
2. Experience: The required or preferred years of experience for the job.
3. Qualifications: The educational qualifications needed for the job.
4. Salary Range: The range of salaries or compensation offered for the position.
5. Location: The city or area where the job is located.
6. Country: The country where the job is located.
7. Latitude: The latitude coordinate of the job location.
8. Longitude: The longitude coordinate of the job location.
9. Work Type: The type of employment (e.g., full-time, part-time, contract).
10. Company Size: The approximate size or scale of the hiring company.
11. Job Posting Date: The date when the job posting was made public.
12. Preference: Special preferences or requirements for applicants (e.g., Only Male or Only Female, or Both)
13. Contact Person: The name of the contact person or recruiter for the job.
14. Contact: Contact information for job inquiries.
15. Job Title: The job title or position being advertised.
16. Role: The role or category of the job (e.g., software developer, marketing manager).
17. Job Portal: The platform or website where the job was posted.
18. Job Description: A detailed description of the job responsibilities and requirements.
19. Benefits: Information about benefits offered with the job (e.g., health insurance, retirement plans).
20. Skills: The skills or qualifications required for the job.
21. Responsibilities: Specific responsibilities and duties associated with the job.
22. Company Name: The name of the hiring company.
23. Company Profile: A brief overview of the company's background and mission.

Potential Use Cases:

• Building predictive models to forecast job market trends.
• Enhancing job recommendation systems for job seekers.
• Developing NLP models for resume parsing and job matching.
• Analyzing regional job market disparities and opportunities.
• Exploring salary prediction models for various job roles.

Acknowledgements:

We would like to express our gratitude to the Python Faker library for its invaluable contribution to the dataset generation process. Additionally, we appreciate the guidance provided by ChatGPT in fine-tuning the dataset, ensuring its quality, and adhering to ethical standards.

Note:

Please note that the examples provided are fictional and for illustrative purposes. You can tailor the descriptions and examples to match the specifics of your dataset. It is not suitable for real-world applications and should only be used within the scope of research and experimentation. You can also reach me via email at: rrana157@gmail.com

Data tables on full-time equivalents (FTEs) in Jersey. These are calculated from the same dataset as the Jersey labour market report. The labour market report is published every six months and covers key aspects of both public and private sector employment. The latest labour market reports can be found here.