Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July 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.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 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.

View monthly updates and historical trends for US Unemployment Rate. from United States. Source: Bureau of Labor Statistics. Track economic data with YCha…

Employment Rate in the United States remained unchanged at 59.60 percent in August. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In June 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends.

Non Farm Payrolls in the United States increased by 22 thousand in August of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

LABOR MARKET ENGAGEMENT INDEXSummary

The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

Interpretation

Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

The Labour Force Survey (LFS) is the only survey conducted by Statistics Canada designed to provide the official unemployment rate every month, with a monthly sample size of approximately 56,000 households. It is the earliest and most timely indicator of the pulse of the labour market in Canada. Statistics Canada provides a Guide to the Labour Force Survey.Note: This dataset primarily focuses on employees: those who do paid work for others. Therefore, totals do not align to totals in Labour Force Characteristics dataset, which focuses on everyone in the labour force.DefinitionsEmployee - A person who does paid work for others.Work - Includes any work for pay or profit, that is, paid work in the context of an employer-employee relationship or self-employment. It also includes work performed by those working in family business without pay (unpaid family workers).Permanent - A permanent job is one that is expected to last as long as the employee wants it, business conditions permitting. That is, there is no predetermined termination date.Temporary - A temporary job has a predetermined end date, or will end as soon as a specified project is completed. Information is collected to allow the sub-classification of temporary jobs into four groups: seasonal; temporary, term or contract, including work done through a temporary help agency; casual job; and other temporary work.Employment - Employed persons are those who, during the reference week, did any work for pay or profit or had a job and were absent from work. Self-employment - Working owners of an incorporated business, farm or professional practice, or working owners of an unincorporated business, farm or professional practice. The latter group also includes self-employed workers who do not own a business (such as babysitters and newspaper carriers). Self-employed workers are further subdivided by those with or without paid help. Also included among the self-employed are unpaid family workers. They are persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling. They represented approximately 1% of the self-employed in 2016.Unemployment - Unemployed persons are those who, during reference week, were without work, were available for work and were either on temporary layoff, had looked for work in the past four weeks or had a job to start within the next four weeks.

Abstract

Focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp)

Analysis unit

Household, Individual.

Universe

The survey covered all the Palestinian persons aged 10 years and above who are a usual residence in State of Palestine

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of this survey is implemented periodically every quarter by PCBS since 1995, where this survey is implemented every quarter in the year (distributed over 13 weeks). The sample is a two-stage stratified cluster sample with two stages: First stage: selection of a stratified sample of 536 EA with (pps) method. Second stage: selection of a random area sample of 15 households from each enumeration area selected in the first stage. The estimated sample size in each quarter was 8,040 households in 2023. The size of the sample was 8,040 households in each quarter of the year 2023, as the survey was conducted according to the designed sample in the West Bank. for Gaza Strip, the survey was implemented in the first three quarters of 2023 only due to the war and the possibility of implementing it in the Gaza Strip.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The lfs questionnaire consists of four main sections: Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

All questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

The response rate was 98.8% in the fourth quarter 2023 in the West Bank only

The response rate was 86.2% in the first quarter 2023 The response rate was 83.7% in the third quarter 2023 The response rate was 83.6% in the second quarter 2023

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators. There is no problem to disseminate results at the national level and at the level of governorates of the West Bank and Gaza Strip.

Data appraisal

The concept of data quality encompasses various aspects, started with planning of the survey to how to publish, understand and benefit from the data. The most important components of statistical quality elements are accuracy, comparability and quality control procedures

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

This dataset is about book subjects. It has 1 row and is filtered where the books is Government and local labour market policy implementation : a report of a study. It features 2 columns including publication dates.

Private businesses in the United States hired 54 thousand workers in August of 2025 compared to 106 thousand in July of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp)

Analysis unit

Household, Individual.

Universe

The survey covered all the Palestinian persons aged 10 years and above who are a usual residence in State of Palestine

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of this survey is implemented periodically every quarter by PCBS since 1995, where this survey is implemented every quarter in the year (distributed over 13 weeks). The sample is a two-stage stratified cluster sample with two stages: First stage: selection of a stratified sample of 536 EA with (pps) method. Second stage: selection of a random area sample of 15 households from each enumeration area selected in the first stage. The estimated sample size in each quarter was 8,040 households in 2022.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The lfs questionnaire consists of four main sections: Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

All questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

The response rate was 68.2% in the fourth quarter 2022 The response rate was 85.6% in 2022 The response rate was 86.1% in the first quarter 2022 The response rate was 85.7% in the third quarter 2022 The response rate was 84.7% in the second quarter 2021

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and at the level of governorates of the West Bank and Gaza Strip.

Data appraisal

The concept of data quality encompasses various aspects, started with planning of the survey to how to publish, understand and benefit from the data. The most important components of statistical quality elements are accuracy, comparability and quality control procedures

Graph and download economic data for Job Openings: Total Nonfarm (JTSJOL) from Dec 2000 to Jul 2025 about job openings, vacancy, nonfarm, and USA.

The primary purpose of the five sets of surveys that comprise the National Longitudinal Surveys is the collection of data on the labor force experience of specific age-sex groups of Americans: Older Men aged 45-59 in 1966, Mature Women aged 30-44 in 1967, Young Men aged 14-24 in 1966, Young Women aged 14-24 in 1968, and Youth aged 14-21 in 1979. Each of the 1960s cohorts has been surveyed 12 or more times over the years, and the Youth cohort has been surveyed yearly since 1979. The major topics covered within the surveys of each cohort include: (1) labor market experience variables (including labor force participation, unemployment, job history, and job mobility), (2) socioeconomic and human capital variables (including education, training, health and physical condition, marital and family characteristics, financial characteristics, and job attitudes), and (3) selected environmental variables (size of labor force and unemployment rates for local area). While the surveys of each cohort have collected data on the above core sets of variables, cohort-specific data have been gathered over the years focusing on the particular stage of labor market attachment that each group was experiencing. Thus, the surveys of young people have collected data on their educational goals, high school and college experiences, high school characteristics, and occupational aspirations and expectations, as well as military service. The surveys of women have gathered data on topics such as fertility, child care, responsibility for household tasks, care of parents, volunteer work, attitudes towards women working, and job discrimination. As the older-aged cohorts of men and women approached labor force withdrawal, surveys for these groups collected information on their retirement plans, health status, and pension benefits. Respondents within the 1979 Youth cohort have been the focus of a number of special surveys, including the collection of data on: (1) last secondary school attended, including transcript information and selected aptitude/intelligence scores, (2) test scores from the Armed Services Vocational Aptitude Battery (ASVAB), (3) illegal activities participation including police contacts, and (4) alcohol use and substance abuse. Finally, the 1986 and 1988 surveys of the Youth cohort included the administration of a battery of cognitive-socioemotional assessments to the approximately 7,000 children of the female 1979 Youth respondents. Data for the five cohorts are provided within main file releases, i.e., Mature Women 1967-1989, Young Women 1968-1991, Young Men 1966-1981, Older Men 1966-1990, and NLSY (Youth) 1979-1992. In addition, the following specially constructed data files are available: (1) a file that specifies the relationships among members of the four original cohorts living in the same household at the time of the initial surveys, i.e., husband-wife, mother-daughter, brother-sister, etc., (2) an NLSY workhistory tape detailing the week-by-week labor force attachment of the youth respondents from 1978 through the most current survey date, (3) an NLSY child-mother file linking the child assessment data to other information on children and mothers within the NLSY, (4) a supplemental NLSY file of constructed and edited fertility variables, (5) a women's support network tape detailing the geographic proximity of the relatives, friends, and acquaintances of 6,308 female NLSY respondents who were interviewed during the 1983-1985 surveys, and (6) two 1989 Mature Women's pension file detailing information on pensions and other employer-provided benefits. (Source: ICPSR, retrieved 07/05/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07610.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The data in this table are based on the Survey labour force (EBB). The EBB is an investigation carried out by CBS is carried out to collect information about the relationship between people and the labour market. Here are the characteristics of persons in relation to with their current or future position in the labour market.

The data in this table refer to unemployment duration. Duration of unemployment refers to the number of months a person is unemployed that’s it. On the basis of the Labour Force Survey, it has been established which persons belonging to the unemployed labour force and since this month these people are unemployed. The month when a person being unemployed is based on the month of the last job (of 12 hours per week or more than one year) or the month from job search (of 12 hours or more per week) or the moment of leaving school.

The figures on the number of long-term unemployed are the result of a first study of the possibilities to arrive at a demarcation of long-term unemployment. Of the unemployed in the Occupational Population Survey (EBB) is known when they have stopped in the last job, when they are

start looking for work and the moment of school leaving. With this data can be found out when someone has become unemployed; this is the start date of unemployment. The unemployment rate is the number of months between the start date of unemployment to the enqûete date. This one study is part of a longer ongoing study on unemployment duration in the Netherlands. These figures therefore have a provisional character and can be adjusted at a later stage.

Due to a new weighing method of the EBB, all EBB tables are stopped and moved to the archive. Instead of being created new tables of which this table is one. In this new tables are the figures with a new weighing method corrected to and 2001. Since 2001, it is also possible to limited set of variables to publish quarterly figures. The years for 2001 have not been corrected and concern the previous published figures. A detailed description of the new weighing method of the EBB can be found on the theme page.

Data available from: 2001

Frequency: discontinued

Changes compared to the previous version: This table has been discontinued and continued in a redesigned table Unemployed labour force; unemployment duration characteristic. The reason for this is new, more accurate figures on duration of unemployment. Corrected for rounding errors in the search time of the unemployed. In addition, the determination of the duration of unemployment no longer an account held at the time of school leaving the unemployed. The old tables on the unemployment duration of the unemployed stopped and moved to the archive. These are the tables; — The unemployed; unemployment Duration — Long-term unemployed; features — Long-term unemployed; region.

When are new figures coming? Stop it.

The data in this table are based on the Labor Force Survey (EBB). The EBB is a survey conducted by Statistics Netherlands to collect information about the relationship between people and the labor market. Characteristics of persons are related to their current or future position on the labor market. The data in this table refer to duration of unemployment. Unemployment duration refers to the number of months that a person is unemployed. Based on the Labor Force Survey, it was determined which persons belong to the unemployed labor force and since which month these persons have been unemployed. The month in which a person became unemployed is based on the month of the last job (of 12 hours per week or more for more than one year) or the month of looking for work (of 12 hours or more per week) or the time of school leaving. The figures on the number of long-term unemployed are the result of an initial study into the possibilities of defining long-term unemployment. The unemployed in the Labor Force Survey (EBB) know when they stopped their last job, when they started looking for work and when they left school. This data can be used to find out when someone became unemployed; this is the start date of unemployment. The duration of unemployment is the number of months between the start date of unemployment and the survey date. This study is part of a longer-term study into unemployment duration in the Netherlands. These figures are therefore provisional and may be adjusted at a later stage. Due to a new weighting method of the EBB, all EBB tables have been discontinued and moved to the archive. Instead, new tables are created. In these new tables, the figures have been corrected up to and including 2001 using a new weighting method. From 2001 it is also possible to publish quarterly figures for a limited set of variables. The years prior to 2001 have not been corrected and are the previously published figures. A detailed description of the new weighing method of the EBB can be found on the theme page. Data available from: 2001 Frequency: discontinued Status of the figures The figures in this publication are provisional. When will new numbers come out? Discontinued.

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-08-30 about initial claims, headline figure, and USA.

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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