The age groups available in the dataset are: 15+, 25+, 25-34, 25-54 and 25-64.

Type of work includes full-time and part-time.

The educational levels include: 0-8 yrs., some high school, high school graduate, some post-secondary, post-secondary certificate diploma and university degree.

Wages include average weekly wage rate.

The immigration statuses include: total landed immigrants (very recent immigrants, recent immigrants, established immigrants), non-landed immigrants and born in Canada.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

In the United States, women holding a bachelor's degree earned, on average, 1,352 U.S. dollars per week in the second quarter of 2024. This can be compared with male bachelor's degree holders who on average earn 1,757 U.S dollars.

In 2023 the mean earnings of Bachelor's degree holders in the United States amounted to 86,970 U.S. dollars. People with higher education degrees tended to earn more than those without. For example, high school graduates, including those with a GED, had mean earnings of 46,720 U.S. dollars.

Average earnings, by age group and highest level of education, from the 2016 Census of Population.

In 2023, the mean income of women with a doctorate degree in the United States stood at 139,100 U.S. dollars. For men with the same degree, mean earnings stood at 175,500 U.S. dollars. On average in 2023, American men earned 91,590 U.S. dollars, while American women earned 65,987 U.S. dollars.

In Sweden, people with a tertiary education have the highest average monthly salaries. In 2022, it amounted to 35,700 Swedish kronor, whereas people with a secondary or primary education earned 34,200 and 31,300, respectively. In 2022, almost 24 percent of the population in Sweden had a tertiary education.

The highest average salaries in financial institutions and insurance companies

The industry with the highest average monthly salary in Sweden in 2022 was financial institutions and insurance companies, where it amounted to over 57,000 Swedish kronor. Even within industries the salaries vary , and the occupational group with the highest average salary in Sweden in is banking, finance and insurance managers.

Women have lower salaries than men

Not only does the salary in Sweden differ between occupations, sectors, industries, and the level of education and age of the employee. Furthermore, men have a higher average salary than women. In 2022, women’s average earnings were 95 percent of men's.

Explore the progression of average salaries for graduates in General Certificate Of Education Ordinary Level Advanced Level High School Equivalent from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of General Certificate Of Education Ordinary Level Advanced Level High School Equivalent relative to other fields. This data is essential for students assessing the return on investment of their education in General Certificate Of Education Ordinary Level Advanced Level High School Equivalent, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in High School Equivalentsecondary Level Studies from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of High School Equivalentsecondary Level Studies relative to other fields. This data is essential for students assessing the return on investment of their education in High School Equivalentsecondary Level Studies, providing a clear picture of financial prospects post-graduation.

In France, in 2020 people who did not possess any kind of diploma had an annual wage income after social contributions of ****** euros. In comparison, the same year, whose who graduated with the baccalauréat, the high school final exam, received around ****** euros. French people who studied for a minimum of three years for a higher education degree, earned the most with ****** euros.

Graph and download economic data for Wage and Salary Workers Paid Hourly Rates: 16 Years and Over: College Graduates: Bachelor's Degree and Higher (BDAHC5) from 2002 to 2024 about paid, tertiary schooling, salaries, workers, hours, 16 years +, education, wages, rate, and USA.

Employment income (in 2019 and 2020) by highest certificate, diploma or degree, for census divisions and municipalities.

Kenya Average Wage Earnings: PR: Education data was reported at 931,440.000 KES in 2017. This records an increase from the previous number of 896,491.700 KES for 2016. Kenya Average Wage Earnings: PR: Education data is updated yearly, averaging 757,345.600 KES from Jun 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 931,440.000 KES in 2017 and a record low of 620,621.700 KES in 2008. Kenya Average Wage Earnings: PR: Education data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.G010: Average Wage Earnings: by Sector and Industry: International Standard of Industrial Classification Rev 4.

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

University graduates had the highest average salary in Russia, at approximately 75.2 thousand Russian rubles per month in 2021. People who did not complete basic general education earned slightly more than those that finished 11 years of school.

Estimated gross annual earnings quartiles for postsecondary graduates working full time at the time of the interview are presented by the province of study, the level of study and gender. Estimates are available at five-year intervals.

Indonesia Monthly Average Wage: BE: Bachelor data was reported at 4,576,111.000 IDR in 2018. This records an increase from the previous number of 4,506,818.000 IDR for 2017. Indonesia Monthly Average Wage: BE: Bachelor data is updated yearly, averaging 1,545,554.000 IDR from Aug 1991 (Median) to 2018, with 27 observations. The data reached an all-time high of 4,576,111.000 IDR in 2018 and a record low of 272,500.000 IDR in 1991. Indonesia Monthly Average Wage: BE: Bachelor data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBB003: Monthly Average Wage: by Education Level.

Indonesia Monthly Average Wage: BE: Urban: Primary School data was reported at 1,732,320.000 IDR in 2018. This records an increase from the previous number of 1,602,501.000 IDR for 2017. Indonesia Monthly Average Wage: BE: Urban: Primary School data is updated yearly, averaging 662,568.500 IDR from Aug 1997 (Median) to 2018, with 22 observations. The data reached an all-time high of 1,732,320.000 IDR in 2018 and a record low of 199,176.000 IDR in 1997. Indonesia Monthly Average Wage: BE: Urban: Primary School data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBB003: Monthly Average Wage: by Education Level.

The Recent College Graduates (RCG) survey estimates the potential supply of newly qualified teachers in the United States and explores the immediate post-degree employment and education experiences of individuals obtaining bachelor's or master's degrees from American colleges and universities. The RCG survey, which focuses heavily, but not exclusively, on those graduates qualified to teach at the elementary and secondary levels, is designed to meet the following objectives: (1) to determine how many graduates become eligible or qualified to teach for the first time and how many are employed as teachers in the year following graduation, by teaching field, (2) to examine the relationship between courses taken, student achievement, and occupational outcomes, and (3) to monitor unemployment rates and average salaries of graduates by field of study. The RCG survey collects information on education and employment of all graduates (date of graduation, field of study, whether newly qualified to teach, further enrollment, financial aid, employment status, and teacher employment characteristics) as well as standard demographic characteristics such as earnings, age, marital status, sex, and race/ethnicity. The 1989-1990 survey (called RCG-91 because the data were collected in 1991) contains four data files. Part 1 contains variables from the main questionnaire and includes information on type of degree received, teaching eligibility, certification, salary, and whether the respondent was unemployed. Also included are transcripts for sampled bachelor degree recipients. Part 2 contains verbatim comments from graduates regarding fields of study, occupation, and parents' occupations. Replicate weights are contained in Part 3, and imputation flags are found in Part 4.

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.