The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical _location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jul 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

South Korea's Occupational Wage Survey (OWS) is an annual business establishment survey conducted since 1970 by South Korea's Ministry of Labor. The dataset contains detailed information on individual workers' earnings, hours worked, educational attainment, actual labor market experience, occupation, industry, and region. The surveyed establishments must employ at least ten workers and were selected by a stratified random sampling method. Because they exclude workers in small enterprises, the self-employed, family workers, temporary workers, and public sector workers, the surveys represent approximately one-half of South Korea's total nonagricultural labor force. The samples for each year are randomly drawn from the original surveys. The surveys cover all industries up through 1986. After 1986, agriculture, forestry, hunting, and fishing are excluded. This change in sampling procedure does not appear to cause a significant change in the types of nonfarm enterprises covered by the survey.

A. SUMMARY This dataset is a cleaned and anonymized version of survey data gathered in FY23 from nonprofits who contract with the City and County of San Francisco. Each row is one employee (representing one filled position), and data includes wages, position detail, position requirements, and demographic information as supplied by the person's employing organization. B. HOW THE DATASET IS CREATED This dataset was generated through conducting a two-tiered survey gathering data on organizational characteristics and position-level data from nonprofits who contract with the City and County of San Francisco in FY23. The survey was fielded in October and November of 2022. 152 nonprofits provided organizational-level data and summary data about their workforce. 29 of those organizations (referred to as "Cohort Organizations") provided additional position-level wage, demographic, and position-requirement data for each worker employed by their organization. This dataset includes position-level data from the cohort organizations. For further details on survey methodology, please review page 39 of the Nonprofit Wage and Equity Survey report, linked below in "Related Reports." C. UPDATE PROCESS Data is a one-time survey and will not update. D. HOW TO USE THIS DATASET Review code book, attached in the "About this Dataset" section. E. RELATED REPORTS This dataset informs the Nonprofit Wage and Equity Survey Report, released by the Controller's Office in April 2023. Nonprofit Wage and Equity Survey F. RELATED DATASETS This dataset is one of two datasets of survey data from the Nonprofit Wage and Equity Survey (FY23). Organization-level data can be viewed on the Open Data Portal. Nonprofit Wage and Equity Survey - Organization-Level Survey Data - FY23

This data comes from the 2018 salary survey data. Please notethe following:

1. Where able, the data has been altered to reflect the annual pay of the average employee in this city and in this position based on the city's salary range.
2. Not all salaries and wages of less than 1 FTE could be confirmed. As a result, some salaries for positions with less than 1 FTE are either the annual equivelant or the actual pay of the employee throughout a year (ex. if an employee would make $50,000 as a full time employee but only works half-time, some cities marked this as $50,000 or $25,000.
3. Educational attainment often was provided with a minimum education and experience as well as a preferred level. The minimum of both categories are provided.

The National Compensation Survey (NCS) provides comprehensive measures of occupational wages; employment cost trends, and benefit incidence and detailed plan provisions. Detailed occupational earnings are available for metropolitan and non-metropolitan areas, broad geographic regions, and on a national basis. The index component of the NCS (ECI) measures changes in labor costs. Average hourly employer cost for employee compensation is presented in the ECEC.

The WageIndicator Survey is a continuous, multilingual, multi-country web-survey, counducted across 65 countries since 2000. The web-survey generates cross sectional and longitudinal data which might provide data especially about wages, benefits, working hours, working conditions and industrial relations. The survey has detailed questions about earnings, benefits, working conditions, employment contracts and training, as well as questions about education, occupation, industry and household characteristics. The WageIndicator Survey is a multilingual questionnaire and aims to collect information on wages and working conditions. As labour markets and wage setting processes vary across countries, country specific translations have been favoured over literal translations. The WageIndicator Survey includes regularly extra survey questions for project targeting specific countries, for specific groups or about specific events. These projects usually address a specific audience (employees of a company, employees in an industry, readers of a magazine, members of a trade union or an occupational association, and alike). The data of the project questions are included in the dataset. Bias: Non-Probability web based surveys are problematic because not every individual has the same probability of being selected into the survey. The probability of being selected depends on national or regional internet access rates and on numbers of visitors accessing the webiste. Data of such surveys form a convenience rather than a probability sample. Due to the non-probability based nature of the survey and its selectivity the obtained results cannot be generalized for the population of interest; i.e. the labor force. Comparisons with representative studies found an underrepresentation of male labour force, part-timers, older age groups, and low educated persons. Besides other strategies to reduce the bias the WageIndicators provides different weighting schemes in order to correct for selection bias. Data Characteristics: The data is organised in annual releases. The data of the period 2000-2005 is released as one dataset. Each data release consists of a dataset with continuous variables and one with project variables. The continuous variables can be merged across years. All variable and value labels are in English. The data does not include the text variables and verbatims form open-ended survey questions, these are available in Excel-Format upon request. Spatial Coverage: The survey started in 2000 in the Netherlands. Since 2004, websites have been launched in many European countries, in North and South America and in countries in Asia. From 2008 on web sites have been launched in more African countries, as well as in Indonesia and in a number of post-Soviet countries. For each country, the questions have been translated. Multilingual countries employ multilingual questionnaires. Country-specific translations and locally accepted terminology have been favored over literal translations.

Forecast: Wages and Salaries in Scientific Research and Development in the US 2022 - 2026 Discover more data with ReportLinker!

Pay structure research (LSO) by origin grouping and level of education. Jobs: at the end of 2002 Hourly wage and monthly wage: December 2002 Annual wages: 2002

Data available from: 2002

Changes as of 24 February 2017: None, this table has been discontinued. No new data will be available from this statistics. The Wage Structure Research was last carried out over the reporting year 2010 and then discontinued. A similar statistic, also over later years, is available from Eurostat, under the name Structure of Earnings Survey. For more information, see the link to Eurostat in paragraph 3.

When are new figures coming? Not applicable.

Graph and download economic data for Employment Level - Agriculture and Related Industries, Wage and Salary Workers (LNU02032184) from Jan 1948 to Jul 2025 about agriculture, salaries, workers, 16 years +, wages, household survey, employment, industry, and USA.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

United States - Employed full time: Wage and salary workers: Survey researchers occupations: 16 years and over: Women was 1.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Survey researchers occupations: 16 years and over: Women reached a record high of 3.00000 in January of 2020 and a record low of 0.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Survey researchers occupations: 16 years and over: Women - last updated from the United States Federal Reserve on July of 2025.

The Occupational Employment and Wage Statistics (OEWS) Survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). The BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OEWS survey make these estimates possible.

The OEWS survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for approximately 830 occupations. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

The OEWS estimates are published annually.

SOURCE: https://www.bls.gov/oes/oes_emp.htm

The Occupational Employment and Wage Statistics (OEWS) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OEWS estimates are constructed from a sample of about 41,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 6,900 sampled establishments, one panel in May and the other in November.

As of May 2023, the average wage for employees in high-tech scientific research and development (R&D) in Israel was ****** Israeli shekels, (about ***** U.S. dollars). This was a slight increase of nearly three percent compared to the previous month, April of the same year. The average wage of employees in this high-tech field in the country fluctuated during the period under review, with an overall slight increase when compared with the base in January 2022, when the average wage was ****** Israeli shekels (around ***** U.S dollars).

United States - Employed full time: Wage and salary workers: Computer and information research scientists occupations: 16 years and over: Women was 12.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Computer and information research scientists occupations: 16 years and over: Women reached a record high of 13.00000 in January of 2021 and a record low of 2.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Computer and information research scientists occupations: 16 years and over: Women - last updated from the United States Federal Reserve on August of 2025.

The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

Envestnet | Yodlee's Payroll Data Panel captures de-identified payroll information to deliver valuable employment insights, such as a company's wage costs, seasonal performance, headcount, hiring, layoffs, and more.

De-identified payroll data analytics for major employers gives decision makers insight into employment trends across many industries. The payroll product includes 1000+ employers and data can be used for company specific or macro purposes. - 4800+ employers tagged - Frequency of payroll identified (i.e. weekly, bi-weekly)
- Data at user and account level to allow for cohort analysis (e.g. Macys likely to lose 10% of revenue due to unemployment within their cohort)

New Features - Mapping to Category codes and Employer Dependency Scoring Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Graph and download economic data for Unemployment Level - Education and Health Services, Private Wage and Salary Workers (LNU03032240) from Jan 2000 to Jul 2025 about health, salaries, workers, education, 16 years +, wages, household survey, services, private, unemployment, and USA.

This data archive consists of a digitized database of all of India's daily minimum wages across states and detailed industries for the years 1983, 1985/86, 1993, 1998, 2004, and 2006. These data are otherwise only available on paper and/or pdf documents. The data are provided as Excel and Stata files, including aggregate codes by broad industry group that the principal investigators assigned during their research.