The 'Non-Farm Payrolls Annual Revision' in the USA is an adjustment made to employment data collected by the Bureau of Labor Statistics, reflecting updated information from more comprehensive sources like unemployment insurance tax records.

Non Farm Payrolls in the United States increased by 22 thousand in August of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In August 2025, the total nonfarm payroll employment increased by around 22,000 people in the United States. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys about 140,000 businesses and government agencies each month, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

Nonfarm Payrolls Private in the United States increased by 38 thousand in August of 2025. This dataset provides the latest reported value for - United States Nonfarm Payrolls - Private - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In August 2025, employment in private education and health services increased by roughly 46,000 in the United States from July 2025. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys about 140,000 businesses and government agencies each month, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

Graph and download economic data for Total Nonfarm Private Payroll Employment (ADPWNUSNERNSA) from 2010-01-09 to 2025-08-16 about payrolls, nonfarm, private, employment, and USA.

View monthly updates and historical trends for US Total Nonfarm Payrolls. from United States. Source: Bureau of Labor Statistics. Track economic data with…

Private businesses in the United States fired -32 thousand workers in September of 2025 compared to -3 thousand in August of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - 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 Quits: Total Nonfarm (JTSQUR) from Dec 2000 to Aug 2025 about quits, nonfarm, and USA.

In August 2025, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 36.53 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.

Graph and download economic data for Job Openings: Total Nonfarm (JTSJOL) from Dec 2000 to Aug 2025 about job openings, vacancy, nonfarm, and USA.

Employees (in Thous) on Non-Farm payrolls by State and selected Industry Sector - Table 5, Part 1 (Totals, Construction & Manufacturing)- Jan-Mar 2008 & Mar 2007 for comparison. Also, see Table 5 Part 2 & 3.

Nonfarm Productivity QoQ in the United States increased to 3.30 percent in the second quarter of 2025 from -1.80 percent in the first quarter of 2025. This dataset includes a chart with historical data for the United States Nonfarm Productivity Qoq.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Aug 2025 about headline figure, establishment survey, manufacturing, employment, and USA.

Abstract (en): This data collection is comprised of data from the 2007 Annual Social and Economic Supplement (ASEC), and is a part of the Current Population Survey (CPS) Series. The Census Bureau conducts the ASEC (known as the Annual Demographic File prior to 2003) over a three-month period, in February, March, and April, with most of the data collected in the month of March. The ASEC uses two sets of survey questions, the basic CPS and a set of supplemental questions.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment.In addition to the basic CPS questions, respondents were asked questions from the ASEC, which provides supplemental data on poverty, geographic mobility/migration, and work experience. Comprehensive work experience information was given on the employment status, occupation, and industry of persons aged 15 and over. Additional data for persons aged 15 and older were available concerning weeks worked and hours per week worked, reason not working full time, total income and supplemental income components. Additional data are included that cover training and assistance received under welfare reform programs such as job readiness training, child care services, or job skill training. Data covering nine noncash income sources: food stamps, school lunch program, employer-provided group health insurance plan, employer-provided pension plan, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance are also included.Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. Data on employment and income refer to the previous calendar year, although demographic data refer to the time of the survey.The original ASEC data provided by the Census Bureau are distributed in a hierarchical file structure, with three record types present: Household, Family, and Person. The ASEC is designed to be a multistage stratified sample of housing units, where the hierarchical file structure can be thought of as a person within a family within a household unit. Here the main unit of analysis is the household unit. For ease of analysis at the person-level, ICPSR created a rectangular file structure that contains a record for every person with the respective Household and Family variables prepended to the Person variables. Part 1 contains the rectangular data file and Part 2 contains the original hierarchical data file. The data contain five weight variables: Basic CPS earnings weight, A_ERNLWT; Basic CPS final weight, A_FNLWGT; March supplement Household weight, HSUP_WGT; March supplement Family weight, FSUP_WGT; ASEC supplement Person weight, MARSUPWT; Users are strongly encouraged to refer to the User Guide for detailed information on how to use the weights, as well as how they were derived. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. The civilian noninstitutional population of the United States living in housing units, and members of the Armed Forces living in civilian housing units on a military base or in a household not on a military base. A multistage probability sample was used for the housing unit. The sample was based on the results of the decennial Census, with coverage in all 50 states and the District of Columbia. More detailed information about the sampling frame can be found in the User Guide. computer-assisted personal interview (CAPI), computer-assisted telephone interview (CATI)ICPSR used syntax created by Jean Roth and the National Bureau of Economic Research (NBER) to read in the data for Part 1.The data in Part 2 are distributed exactly as they arrived from the data depositor. ICPSR...

Non Farm Payrolls in the United Kingdom decreased by 10 thousand in September of 2025. This dataset provides - United Kingdom Payrolled Employees- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Aug 2025 about earnings, establishment survey, average, hours, wages, private, employment, and USA.

View monthly updates and historical trends for ADP Employment Change. from United States. Source: ADP. Track economic data with YCharts analytics.

Graph and download economic data for All Employees, Total Private (USPRIV) from Jan 1939 to Aug 2025 about headline figure, establishment survey, private industries, private, employment, industry, and USA.