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.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

In October 2024, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 35.46 U.S. dollars. The data have been seasonally adjusted. Employed persons are employees on nonfarm payrolls and consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

The project, funded by the National Science Foundation (NSF) GRANTED program, focuses on identifying and addressing the critical needs and challenges faced by the research administrator workforce across institutions and organizations in the United States. The overarching goal is to develop a national job classification system that standardizes job titles, roles, and salary structures for research administrators. This initiative stems from the recognition that research administrators play a pivotal role in supporting faculty and enhancing research productivity. However, inconsistencies in job classifications and compensation have led to high turnover rates and recruitment challenges, particularly exacerbated by the COVID-19 pandemic. The project involved a comprehensive approach, including a virtual focus group, survey, and workshop, to gather diverse perspectives and develop actionable solutions. By creating a standardized system, the project seeks to improve recruitment, retention, and overall workforce effectiveness, contributing to the success and innovation of research enterprises nationwide. The following files are available and shared:1. Survey Instrument - This deposit includes the complete survey instrument used in the NSF-funded project. The survey is designed to collect data on the roles, responsibilities, and challenges faced by research administrators, aiming to gather insights to inform the creation of a national job classification system. The survey was distributed nationally using SurveyMonkey to a broad range of research administrators and was open for participation from June 17 to July 17, 2024. Distribution lists included the Society of Research Administrators International (SRAI) membership, National Council of University Research Administrators (NCURA) membership, NSF GRANTED listserv, Research Administration Listserv (RESADM-L), focus group participants, and the Midwest Research and Graduate Administrators Forum. Administered by SRAI on behalf of Jennifer Woodward and Evan Roberts, the survey remained open for 30 days with one reminder. No compensation was provided for participation. Questions 35 and 36 in the survey were adapted with permission from the CUPA-HR Employee Retention Survey. Sharing this instrument supports transparency and allows others to replicate and build upon the study.2. Survey Data - The survey data deposit contains the raw dataset collected using the survey instrument. The data includes responses from 2,441 research administrators across various institutions, providing a comprehensive view of their experiences and challenges. This dataset is essential for analyzing trends and validating findings that will contribute to the development of a national job classification system. The data are anonymized and shared to foster collaboration in addressing workforce issues and enhancing research administration effectiveness.3. Survey Analysis Slides - The slides summarize the analysis of the survey data. The slides present key findings and insights into the needs and challenges faced by research administrators, including issues related to job title consistency, roles and responsibilities, and salary structures. Visual representations, such as charts and graphs, illustrate the analysis of survey responses, highlighting significant challenges and potential solutions. These slides are shared to communicate the project's outcomes and support informed discussions on improving research administration through standardization and strategic initiatives.

Brazil Average Real Income: All Jobs: Usual Earnings data was reported at 2,295.000 BRL in Apr 2019. This records a decrease from the previous number of 2,304.000 BRL for Mar 2019. Brazil Average Real Income: All Jobs: Usual Earnings data is updated monthly, averaging 2,254.500 BRL from Mar 2012 (Median) to Apr 2019, with 86 observations. The data reached an all-time high of 2,312.000 BRL in Feb 2019 and a record low of 2,159.000 BRL in Mar 2012. Brazil Average Real Income: All Jobs: Usual Earnings data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates 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.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

Wages in the United States increased 4.72 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Economically Active Population Survey: Average wages of the main job by period, type of working day, underemployment and decile. Annual. National.

The National Survey of College Graduates is a repeated cross-sectional biennial survey that provides data on the nation's college graduates, with a focus on those in the science and engineering workforce. This survey is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

Number of UK jobs paid below minimum wage by sex, age, occupation and industry, and region, annual estimates, 1998 to 2023. Annual Survey of Hours and Earnings.

Brazil Average Nominal Income: All Jobs: Usual Earnings data was reported at 2,295.000 BRL in Apr 2019. This records an increase from the previous number of 2,291.000 BRL for Mar 2019. Brazil Average Nominal Income: All Jobs: Usual Earnings data is updated monthly, averaging 1,892.000 BRL from Mar 2012 (Median) to Apr 2019, with 86 observations. The data reached an all-time high of 2,295.000 BRL in Apr 2019 and a record low of 1,434.000 BRL in Mar 2012. Brazil Average Nominal Income: All Jobs: Usual Earnings data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.

Economically Active Population Survey: Average wages of the main job by period, type of working day, sector of educational level attained and decile. Annual. National.

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

In 2023, the median hourly earnings of wage and salary workers in the United States was 19.24 U.S. dollars. This is an increase from 1979, when median hourly earnings were at 4.44 U.S. dollars. Hourly Workers The United States national minimum wage is 7.25 U.S. dollars per hour, which has been the minimum wage since 2009. However, each state has the agency to set their state minimum wage. Furthermore, some cities are able to create their minimum wage. Many argue that the minimum wage is too low and should be raised, because it is not considered a living wage. There has been a movement to raise the minimum wage to 15 U.S. dollars per hour, called “Fight for 15” which began in the early 2010s. While there has been no movement at the federal level, some states have moved to increase their minimum wages, with at least three states and the District of Columbia setting minimum wage rates at or above 15 dollars per hour. More recently, some proponents of increasing the minimum wage say that 15 dollars is too low, and lawmakers should strive toward a higher goal, especially given that a 2021 analysis found that the minimum wage in the U.S. should be 22.88 U.S. dollars if it grew at the same rate as economic productivity. Salary Workers On the other hand, salary workers in the United States do not get paid on an hourly basis. The median weekly earnings of salary workers have significantly increased since 1979. Asian salary workers had the highest hourly earnings in the U.S. in 2021. Among female salary workers, those ages 45 to 54 years old had the highest median hourly earnings in 2021, likewise for male salary workers.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within National Park. The dataset can be utilized to gain insights into gender-based income distribution within the National Park population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within National Park, among individuals aged 15 years and older with income, there were 1,132 men and 1,064 women in the workforce. Among them, 569 men were engaged in full-time, year-round employment, while 437 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.34% fell within the income range of under $24,999, while 8.70% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 16.87% of men in full-time roles earned incomes exceeding $100,000, while 17.39% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for National Park median household income by race. You can refer the same here

Estimates of the number and proportion of UK employee jobs with hourly pay below the living wage, by region, work geography, local authority and Parliamentary constituency, as defined by the Living Wage Foundation.

The National Survey of College Graduates (NSCG) is a biennial dataset sponsored by the U.S. National Science Foundation (NSF) and conducted by the Census Bureau since the 1970s. It captures detailed information on U.S. residents aged under 76 with at least a bachelor’s degree, focusing on demographics, employment, salaries, work activities, and educational backgrounds. The survey emphasizes graduates in science and engineering (S&E) fields, though it includes all degree holders. Its primary purpose is to provide data for policymakers, researchers, and institutions to analyze workforce trends, evaluate educational outcomes, and inform decisions on workforce development and education policy. Key features include longitudinal tracking of degree-field-occupation relationships, detailed labor market insights, and public microdata files dating back to 1993. The dataset is released every two years, with 2023 being the most recent iteration, and includes specialized tools for examining the impact of factors like the COVID-19 pandemic on career trajectories. Unique aspects include its focus on S&E workforce dynamics and granular data on salary ranges and job activities, making it a critical resource for understanding the economic contributions of college-educated professionals.