Wages in Manufacturing in the United States increased to 28.64 USD/Hour in February from 28.54 USD/Hour in January of 2025. This dataset provides - United States Average Hourly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average Hourly Earnings in the United States increased 0.30 percent in February 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.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CEU0500000003) from Mar 2006 to Feb 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

This dataset provides values for AVERAGE HOURLY EARNINGS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

Average full-time hourly wage paid and payroll employment by type of work, economic region and National Occupational Classification (NOC), 2016 and 2017.

Average hourly earnings for employees paid by the hour, by North American Industry Classification System (NAICS) and overtime status, last 5 years.

Average Hourly Earnings in Ireland increased to 30.21 EUR in December of 2024 over the previous month. This dataset provides - Ireland Average Hourly Earnings- actual values, historical data, forecast, chart, statistics, economic calendar and news.

2004 to 2017 annual data source: ABS characteristics of employment cat no. 6333.0 2004 to 2017 annual data source: ABS characteristics of employment cat no. 6333.0

Series Name: Average hourly earnings of employees by sex and occupation (local currency)Series Code: SL_EMP_AEARNRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilitiesTarget 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueGoal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This table contains 1339 series, with data for years 1961 - 1983 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (62 items: Canada; Newfoundland and Labrador; Atlantic provinces ...), Wage earners (2 items: Average weekly hours; Average hourly earnings ...), Standard Industrial Classification, 1960 (SIC) (124 items: Mining; including milling; Metals; Gold; Copper-gold-silver ...).

Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Data Resources (4) CSV Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Preview Download JSON-STAT Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Preview Download PX Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Details Download XLSX Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours

Average hourly pay, pay dispersion etc., manual workers private sector (SLP) by occupation, sex, observations and year

This dataset represents the statewide average hourly wage of Department of Rehabilitation’s total successful closures in State Fiscal Years 2014 through 2023 by county.

This study examines the short-term effects of the introduction of a statutory minimum wage in Germany on hourly wages, monthly wages and paid working hours. We exploit a novel panel dataset by linking the Structure of Earnings Survey (SES) 2014 and the Earn-ings Survey (ES) 2015 and apply a difference-in-differences approach at the establishment level. The results indicate an effect of the introduction of the statutory minimum wage on the average hourly wages of employees in minimum wage establishments of up to 5.9 percent. Due to negative effects on average working time of approximately minus 3.1 percent, the effects on monthly gross earnings are smaller but still amount to up to 2.7 percent on aver-age. The results further suggest that the minimum wage effects on earnings were greater among low-wage employees than on average, in eastern Germany than in western Germany, and among part-time employees and marginal employees than among full-time employees.

Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Data Resources (4) CSV Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Preview Download JSON-STAT Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Preview Download PX Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours Details Download XLSX Mean Hourly Earnings, Weekly Earnings and Weekly Paid Hours

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.

This dataset contains data on annual average hourly earnings of industrial workers in Estonia in 1919-1939. Dataset "Annual Average Hourly Earnings of Industrial Workers in Estonia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

covers 2004 to 2017 annual data source: Australian Bureau of Statistics cat no. 6333.0 tbls 3 and 4.

Average hourly wage at closure on cases where the individual was competitively employed after receiving services from Iowa Vocational Rehabilitation Services