Wages in the United States increased to 31.24 USD/Hour in June from 31.15 USD/Hour in May of 2025. This dataset provides - United States Average Hourly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Manufacturing in the United States remained unchanged at 28.87 USD/Hour in June. This dataset provides - United States Average Hourly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

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!

Average Hourly Earnings in the United States increased 0.20 percent in June of 2025 over the previous month. This dataset provides the latest reported value for - United States Average Hourly Earnings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average weekly earnings, average hourly wage rate and average usual weekly hours by union status and type of work, last 5 years.

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.

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Average Weekly Salary: ML: Support Activity data was reported at 1,476.340 USD in Sep 2018. This records an increase from the previous number of 1,462.860 USD for Aug 2018. United States Average Weekly Salary: ML: Support Activity data is updated monthly, averaging 1,227.550 USD from Mar 2006 (Median) to Sep 2018, with 151 observations. The data reached an all-time high of 1,499.500 USD in Jun 2018 and a record low of 972.560 USD in Mar 2006. United States Average Weekly Salary: ML: Support Activity data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

This dataset provides information on poverty-level wages in the United States from 1973 to 2022.

It includes data on both annual and hourly poverty-level wages, as well as wage shares for different income brackets.

The dataset is based on the Economic Policy Institute’s State of Working America Data Library, which offers comprehensive economic data for analyzing trends and patterns in the labor market.

Intresting Task Ideas:

1. Analyze the trend of annual poverty-level wages over the years.
2. Compare the hourly poverty-level wages between men and women.
3. Investigate the share of workers earning below poverty-level wages based on race.
4. Explore the distribution of wages across different poverty wage ranges.
5. Examine the income disparities across different income brackets (0-75%, 75-100%, etc.) for men and women.
6. Determine the proportion of workers earning above the poverty level (300%+) over time.
7. Calculate the percentage change in poverty-level wages from one year to another.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

USA Wage Comparison for College vs. High School

Productivity and Hourly Compensation

150K TMDb TV Shows

USA Unemployment Rates by Demographics & Race

Gender Wage Gap in the USA

Photo by Jon Tyson on Unsplash

Wages in Manufacturing in Mexico increased to 4 USD/Hour in April from 3.90 USD/Hour in March of 2025. This dataset provides - Mexico Average Wages in Manufacturing Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Average Weekly Salary: Mining and Logging (ML): Mining data was reported at 1,541.870 USD in Oct 2018. This records a decrease from the previous number of 1,579.590 USD for Sep 2018. United States Average Weekly Salary: Mining and Logging (ML): Mining data is updated monthly, averaging 1,290.835 USD from Mar 2006 (Median) to Oct 2018, with 152 observations. The data reached an all-time high of 1,579.590 USD in Sep 2018 and a record low of 1,001.730 USD in Mar 2006. United States Average Weekly Salary: Mining and Logging (ML): Mining data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

As of 2023, the average annual wage of Germany was 48,301 euros per year, a growth of almost 6,000 Euros when compared with 2000. From 2000 until 2007, wages rose by less than a thousand euros, with wage growth accelerating mainly in the period after 2010. Comparisons with rest of the EU Within the European Union Luxembourg had an average annual salary of almost 80 thousand Euros, with Germany having an annual salary comparable to other large European Countries, such as the United Kingdom and France. In neighboring Poland, the average annual salary was just over 39 thousand U.S dollars, meaning that German’s earned, on average, 20 percent more than what their Polish counterparts did. German economy slowing in 2023 While Germany initially had one of the strongest recoveries from the 2008 financial crash and as of 2020 had the largest economy in Europe its economy has started to slow in recent years. For 2023 the German economy is contracted by 0.26 percent, and while 2024 marked a slight improvement, the expectations are that 2025 remains a year of slow growth.

Average Hourly Earnings YoY in the United States decreased to 3.70 percent in June from 3.80 percent in May of 2025. This dataset includes a chart with historical data for the United States Average Hourly Earnings YoY.

Analysis of ‘US Minimum Wage by State from 1968 to 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

US Minimum Wage by State from 1968 to 2020

The Basics

• What is this? In the United States, states and the federal government set minimum hourly pay ("minimum wage") that workers can receive to ensure that citizens experience a minimum quality of life. This dataset provides the minimum wage data set by each state and the federal government from 1968 to 2020.

• Why did you put this together? While looking online for a clean dataset for minimum wage data by state, I was having trouble finding one. I decided to create one myself and provide it to the community.

• Who do we thank for this data? The United States Department of Labor compiles a table of this data on their website. I took the time to clean it up and provide it here for you. :) The GitHub repository (with R Code for the cleaning process) can be found here!

Content

This is a cleaned dataset of US state and federal minimum wages from 1968 to 2020 (including 2020 equivalency values). The data was scraped from the United States Department of Labor's table of minimum wage by state.

Description of Data

The values in the dataset are as follows: - Year: The year of the data. All minimum wage values are as of January 1 except 1968 and 1969, which are as of February 1. - State: The state or territory of the data. - State.Minimum.Wage: The actual State's minimum wage on January 1 of Year. - State.Minimum.Wage.2020.Dollars: The State.Minimum.Wage in 2020 dollars. - Federal.Minimum.Wage: The federal minimum wage on January 1 of Year. - Federal.Minimum.Wage.2020.Dollars: The Federal.Minimum.Wage in 2020 dollars. - Effective.Minimum.Wage: The minimum wage that is enforced in State on January 1 of Year. Because the federal minimum wage takes effect if the State's minimum wage is lower than the federal minimum wage, this is the higher of the two. - Effective.Minimum.Wage.2020.Dollars: The Effective.Minimum.Wage in 2020 dollars. - CPI.Average: The average value of the Consumer Price Index in Year. When I pulled the data from the Bureau of Labor Statistics, I selected the dataset with "all items in U.S. city average, all urban consumers, not seasonally adjusted". - Department.Of.Labor.Uncleaned.Data: The unclean, scraped value from the Department of Labor's website. - Department.Of.Labor.Cleaned.Low.Value: The State's lowest enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.High.Value are identical. (Some states enforce different minimum wage laws depending on the size of the business. In states where this is the case, generally, smaller businesses have slightly lower minimum wage requirements.) - Department.Of.Labor.Cleaned.Low.Value.2020.Dollars: The Department.Of.Labor.Cleaned.Low.Value in 2020 dollars. - Department.Of.Labor.Cleaned.High.Value: The State's higher enforced minimum wage on January 1 of Year. If there is only one minimum wage, this and the value for Department.Of.Labor.Cleaned.Low.Value are identical. - Department.Of.Labor.Cleaned.High.Value.2020.Dollars: The Department.Of.Labor.Cleaned.High.Value in 2020 dollars. - Footnote: The footnote provided on the Department of Labor's website. See more below.

Data Footnotes

As laws differ significantly from territory to territory, especially relating to whom is protected by minimum wage laws, the following footnotes are located throughout the data in Footnote to add more context to the minimum wage. The original footnotes can be found here.

--- Original source retains full ownership of the source dataset ---

Wages in Brazil increased to 3457 BRL/Month in May from 3441 BRL/Month in April of 2025. This dataset provides - Brazil Average Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Kenya Monthly Minimum Wages: Average: USD data was reported at 193.544 USD in 2017. This records an increase from the previous number of 168.507 USD for 2016. Kenya Monthly Minimum Wages: Average: USD data is updated yearly, averaging 144.445 USD from Jun 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 193.544 USD in 2017 and a record low of 92.870 USD in 2005. Kenya Monthly Minimum Wages: Average: USD 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.G008: Monthly Minimum Wages. CEIC converts monthly Minimum Wage into USD. The Kenya National Bureau of Statistics provides monthly Minimum Wage in local currency for Nairobi, Mombasa and Kisumu. The Central Bank of Kenya average market exchange rate is used for currency conversions. Minimum Wage is in annual frequency - ending June of each year.

Wages in Mexico decreased to 278.93 MXN/Day in May from 621.89 MXN/Day in April of 2025. This dataset provides - Mexico Average Daily Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Bachelor's degree and higher: 25 years and over (LEU0252918500Q) from Q1 2000 to Q1 2025 about second quartile, full-time, 25 years +, tertiary schooling, salaries, workers, earnings, education, wages, median, employment, and USA.