As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

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

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

This graph displays the twenty largest occupation groups in the United States as of May 2023, ranked by annual mean wage. The annual mean wage among the 7.7 million retail sales workers in the U.S. stood at 34,520 U.S. dollars in 2023.

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

Graph and download economic data for Personal Taxes: Federal Income Taxes by Occupation: Wage and Salary Earners: Construction Workers and Mechanics (CXUFEDTAXESLB1207M) from 1984 to 2023 about mechanics, occupation, salaries, workers, tax, construction, federal, wages, personal, income, and USA.

Data on employment income statistics, by occupation minor group (4-digit code) from the National Occupational Classification (NOC) 2021, immigrant status and period of immigration, highest level of education, work activity during the reference year, age and gender, for the population aged 15 years and over who reported weeks worked and employment income in 2020 in private households in Canada, provinces and territories.

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.

In 2022, the median weekly earnings were lowest in the service occupations across all ethnicities. In the management, professional, and related occupations category, a white worker's weekly earnings amounted to around 1,469 U.S. dollars, compared to that of a Black or African American workers weekly earnings which amounted to around 1,210 U.S. dollars.

Graph and download economic data for Income Before Taxes: Interest, Dividends, Rent Income, Property Income by Occupation: Wage and Salary Earners: Managers and Professionals (CXUINDIVRNTLB1204M) from 1984 to 2023 about dividends, management, occupation, professional, rent, salaries, tax, wages, interest, income, and USA.

Graph and download economic data for Income Before Taxes: Self-Employment Income by Occupation: Wage and Salary Earners: Service Workers (CXUSFEMPINCLB1206M) from 1984 to 2023 about self-employed, occupation, salaries, workers, tax, wages, services, income, and USA.

In 2023, with the exception of the professional category of armed forces, police and military firefighters, women's earnings in Brazil were always lower than men's. In 2023, a male manager earned an average of ***** Brazilian reals per month, while a female manager earned an average of about ***** reals per month.

This statistic highlights the average net monthly salary of French employees in the private sector in 2022, by socio-professional category and by gender. In 2022, a male manager earned on average 4,770 euros per month, while a female executive earned on average around 4,020 euros per month.

In 2023, the highest average monthly wages in Norway were in the mining and quarrying industry, with nearly 87,000 Norwegian kroner. The second highest average wages were in the financial and insurance industry, followed by the electricity, gas, and steam industry. That year, the lowest average salaries were in the accommodation and food service industry, counting only 38,000 Norwegian kroner per month.

In Mexico as of the third quarter of 2023, the sectors of occupation measured by the average monthly salary had the extractive industry as the clear leader, in terms of highest average salary, with 10,612 Mexican pesos, followed by the governmental, education and health areas.

The median income³ of full-time employees⁴ in manager level positions in Switzerland was ******* Swiss francs gross in 2023. Therefore, this was the best-earning occupational group in Switzerland, followed by academic professions, with a median income of ******* Swiss francs. The lowest earning professions, apart from apprentices, were jobs in unskilled labor, with median gross earnings of ******.

As of August 2023, the highest average monthly net wage for both male and female workers in Indonesia was from working as leadership and management personnel, with values of around **** million and **** million Indonesian rupiah, respectively. Overall, male workers had a higher average monthly income compared to female workers. The highest pay gap was found in the professional, technical, and related sectors, where female workers received around ***** percent lower pay than male workers.

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.

Graph and download economic data for Income Before Taxes: Wages and Salaries by Occupation: Wage and Salary Earners: Managers and Professionals (CXU900000LB1204M) from 1984 to 2023 about management, occupation, professional, salaries, tax, wages, income, and USA.

Data on employment income statistics by occupation minor group (4-digit code) from the National Occupational Classification (NOC) 2021, Indigenous identity, highest level of education, work activity during the reference year, age and gender, for the population aged 15 years and over who reported weeks worked and employment income in 2020, in private households in Canada, provinces and territories and census metropolitan areas with parts.