In October 2024, the total nonfarm payroll employment increased by around 12,000 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.

United States Employment: Non Farm Payroll: sa: MoM data was reported at 151.000 Person th in Feb 2025. This records an increase from the previous number of 125.000 Person th for Jan 2025. United States Employment: Non Farm Payroll: sa: MoM data is updated monthly, averaging 161.000 Person th from Feb 1939 (Median) to Feb 2025, with 1033 observations. The data reached an all-time high of 4,631.000 Person th in Jun 2020 and a record low of -20,471.000 Person th in Apr 2020. United States Employment: Non Farm Payroll: sa: MoM data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G056: Current Employment Statistics Survey: Employment: Non Farm Payroll: sa.

Welcome to the official source for Employee Payroll Costing data for the City of Chicago. This dataset offers a clean, comprehensive view of the City's payroll information by employee. About the Dataset: This has been extracted from the City of Chicago's Financial Management and Purchasing System (FMPS). FMPS is the system used to process all financial transactions made by the City of Chicago, ensuring accuracy and transparency in fiscal operations. This dataset includes useful details like employee name, pay element, pay period, fund, appropriation, department, and job title. Data Disclaimer: The following data disclaimer governs your use of the dataset extracted from the Payroll Costing module of the City of Chicago's Financial Management and Purchasing System (FMPS) or (FMPS Payroll Costing). Point-in-Time Extract: The dataset provided herein, represents a point-in-time extract from the FMPS Payroll Costing module and may not reflect real-time or up-to-date data. Financial Statement Disclaimer – Timeframe and Limitations: This dataset is provided without audit. It is essential to note that this dataset is not a component of the City's Annual Comprehensive Financial Report (ACFR). As such, it remains preliminary and is subject to the end-of-year reconciliation process inherent to the City's annual financial procedures outlined in the ACFR. Note on Pay Elements: All pay elements available in the FMPS Payroll Costing module have been included in this dataset. Previously published datasets, such as "Employee Overtime and Supplemental Earnings," contained only a subset of these pay elements. Payroll Period: The dataset's timeframe is organized into 24 payroll periods. It is important to understand that these periods may or may not directly correspond to specific earnings periods. Aggregating Data: The CIty of Chicago often has employees with the same name (including middle initials). It is vital to use the unique employee identifier code (EMPLOYEE DATASET ID) when aggregating at the employee level to avoid duplication. Data Subject to Change: This dataset is subject to updates and modifications due to the course of business, including activities such as canceling, adjusting, and reissuing checks. Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

In October 2024, employment in private education and health services increased by roughly 57,000 in the United States from September 2024. 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.

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

Detailed compensation data for state employees from FY 2010-2014. Payroll data from 2015 through the present is available in this dataset: State Employee Payroll Data Calendar Year 2015 through Present (https://data.ct.gov/Government/State-Employee-Payroll-Data-Calendar-Year-2015-thr/virr-yb6n)

Employment: NF: PB: Nanotechnology data was reported at 22.900 Person th in Jan 2025. This records a decrease from the previous number of 23.600 Person th for Dec 2024. Employment: NF: PB: Nanotechnology data is updated monthly, averaging 20.000 Person th from Jan 1990 (Median) to Jan 2025, with 421 observations. The data reached an all-time high of 27.900 Person th in Aug 2023 and a record low of 15.700 Person th in Jan 1997. Employment: NF: PB: Nanotechnology data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G053: Current Employment Statistics Survey: Employment: Non Farm Payroll.

Graph and download economic data for Indexes of Aggregate Weekly Payrolls of All Employees, Total Private (CES0500000017) from Mar 2006 to Feb 2025 about payrolls, establishment survey, private, employment, indexes, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Waiters and waitresses occupations: 16 years and over (LEU0254493400A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

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

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals. NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data. NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

Graph and download economic data for Average Hourly Earnings of All Employees, Manufacturing (CES3000000003) from Mar 2006 to Feb 2025 about earnings, establishment survey, hours, wages, manufacturing, employment, and USA.

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.

Employment: NF: sa: IF: WS: Web Search Portals & Others data was reported at 155.600 Person th in Jan 2025. This records an increase from the previous number of 154.600 Person th for Dec 2024. Employment: NF: sa: IF: WS: Web Search Portals & Others data is updated monthly, averaging 57.800 Person th from Jan 1990 (Median) to Jan 2025, with 421 observations. The data reached an all-time high of 162.700 Person th in Dec 2022 and a record low of 24.600 Person th in Jan 1990. Employment: NF: sa: IF: WS: Web Search Portals & Others data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G056: Current Employment Statistics Survey: Employment: Non Farm Payroll: sa.

Envestnet | Yodlee's Salary Data Panel captures de-identified payroll information to deliver valuable employment insights, such as a company's wage costs, seasonal performance, headcount, hiring, layoffs, and more.

De-identified payroll data analytics for major employers gives decision makers insight into employment trends across many industries. The payroll product includes 1000+ employers and data can be used for company specific or macro purposes. - 4800+ employers tagged - Frequency of payroll identified (i.e. weekly, bi-weekly)
- Data at user and account level to allow for cohort analysis (e.g. Macys likely to lose 10% of revenue due to unemployment within their cohort)

New Features - Mapping to Category codes and Employer Dependency Scoring Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

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Average full-time hourly wage paid and payroll employment by type of work, North American Industry Classification System (NAICS) and National Occupational Classification (NOC), 2016 and 2017.

Wages in the United States increased 4.46 percent in January 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.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.