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.

The Title and Salary Listing is a compilation of job titles under the jurisdiction of the Department of Civil Service.

City employee Base and Overtime Salary by Fiscal Year.

City employee Base and Overtime Salary by Fiscal Year.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that include salary and compensation data. The dataset consists of salary data by employee reported by State Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes salary and compensation data. The dataset consists of salary data by employee reported by Local Development Corporations that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes salary and compensation data. The dataset consists of salary data by employee reported by Industrial Development Agencies that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Graph and download economic data for Average Hourly Earnings of All Employees: Total Private in New York City, NY (SMU36935610500000003) from Jan 2007 to Aug 2025 about New York, earnings, hours, NY, private, employment, and USA.

The County of Broome Personnel Department produces areport annually containing the Name, Title, Department,Base Earnings and Over Time Earnings of each employee who has held a position within the County anytime duringthat year.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that include salary and compensation data. The dataset consists of salary data by employee reported by State Authorities beginning with fiscal years ending in 2011.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for new york in the U.S.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes salary and compensation data. The dataset consists of salary data by employee reported by Local Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The Occupational Employment and Wage Statistics (OEWS) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OEWS estimates are constructed from a sample of about 41,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 6,900 sampled establishments, one panel in May and the other in November.

Bi-Weekly report of monies disbursed to Senate employees. The report is issued by the Secretary of the Senate and is available as both a .csv file and it's original PDF format for download at the bottom of the page. The Report includes fields for the employee name, the employing office, city of employment, the employee title, the employee biweekly or hourly rate (whichever applies), the employee payroll type, the pay period, the pay period's beginning and end date, the check date and the employing legislative entity.

In 2023, the average wage in private industry in Alabama was at 58,993 U.S. dollars. This was nearly half of the annual average wages per employee in the District of Columbia, at 112,048 U.S. dollars. That year, the District of Columbia, Massachusetts, and New York were the top ranked states in terms of average wages per employee.

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.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for state university of new york at buffalo in the U.S.

The Civil List reports the agency code (DPT), first initial and last name (NAME), agency name (ADDRESS), title code (TTL #), pay class (PC), and salary (SAL-RATE) of individuals who were employed by the City of New York at any given time during the indicated year.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for law applicant licensed to practice law in the state of ny and ca in the U.S.

The EEOC collects workforce data from employers with more than 100 employees, including State and Local governments. Federal agencies use the report to develop new strategies in furtherance of EEO practices, helping jurisdictions to establish benchmarks and provide guidance in the evaluation of internal programs to ensure equal employment opportunity. The City of New York is legally mandated to submit the federal EEO-4 report every two years. The report provides a summary of a jurisdiction's workforce composition by agency function, job category, salary, race/ethnicity, and gender.