Graph and download economic data for All Employees, Federal (CES9091000001) from Jan 1939 to Jul 2025 about establishment survey, federal, government, employment, and USA.

Government Payrolls in the United States decreased by 10 thousand in July of 2025. This dataset provides the latest reported value for - United States Government Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Federal Way. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Federal Way, the median income for all workers aged 15 years and older, regardless of work hours, was $49,179 for males and $34,280 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in Federal Way. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Federal Way.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Federal Way.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Way median household income by race. You can refer the same here

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Number of employees by North American Industry Classification System (NAICS) and type of employee, last 5 years.

United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data was reported at 17.169 Per 1000 in 2020. This records an increase from the previous number of 15.152 Per 1000 for 2019. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data is updated yearly, averaging 13.282 Per 1000 from Dec 2011 (Median) to 2020, with 10 observations. The data reached an all-time high of 17.169 Per 1000 in 2020 and a record low of 12.478 Per 1000 in 2012. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

Percentage of workforce laid off because of COVID-19, by North American Industry Classification System (NAICS) code, business employment size, type of business and majority ownership.

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Federal Heights. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Federal Heights, the median income for all workers aged 15 years and older, regardless of work hours, was $39,520 for males and $27,343 for females.

These income figures highlight a substantial gender-based income gap in Federal Heights. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Federal Heights.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Federal Heights.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Heights median household income by race. You can refer the same here

The Occupational Safety and Health Administration (OSHA) collected work-related injury and illness data from employers within specific industry and employment size specifications from 2002 through 2011. This data collection is called the OSHA Data Initiative or ODI. The data provided is used by OSHA to calculate establishment specific injury and illness incidence rates. This searchable database contains a table with the name, address, industry, and associated Total Case Rate (TCR), Days Away, Restricted, and Transfer (DART) case rate, and the Days Away From Work (DAFWII) case rate for the establishments that provided OSHA with valid data for calendar years 2002 through 2011. This data has been sampled down from its original size to 4%. In addition, the original dataset only has data from a small portion of all private sector establishments in the United States (80,000 out of 7.5 million total establishments). Therefore, these data are not representative of all businesses and general conclusions pertaining to all US business should not be overdrawn. Data quality: While OSHA takes multiple steps to ensure the data collected is accurate, problems and errors invariably exist for a small percentage of establishments. OSHA does not believe the data for the establishments with the highest rates on this file are accurate in absolute terms. Efforts were made during the collection cycle to correct submission errors, however some remain unresolved. It would be a mistake to say establishments with the highest rates on this file are the ‘most dangerous’ or ‘worst’ establishments in the Nation. Rate Calculation: An incidence rate of injuries and illnesses is computed from the following formula: (Number of injuries and illnesses X 200,000) / Employee hours worked = Incidence rate. The Total Case Rate includes all cases recorded on the OSHA Form 300 (Column G + Column H + Column I + Column J). The Days Away/Restriced/Transfer includes cases recorded in Column H + Column I. The Days Away includes cases recorded in Column H. For further information on injury and illness incidence rates, please visit the Bureau of Labor Statistics’ webpage at http://www.bls.gov/iif/osheval.htm State Participation: Not all state plan states participate in the ODI. The following states did not participate in the 2010 ODI (collection of CY 2009 data), establishment data is not available for these states: Alaska; Oregon; Puerto Rico; South Carolina; Washington; Wyoming.

Data Dictionary

Acknowlegement

Original Data

CORGIS Dataset Project

Foto von National Cancer Institute auf Unsplash

Judgement on economic and social conditions in the USA in comparison to the FRG. Topics: Development of personal economic conditions and the standard of living in the FRG; reasons for the so-called economic miracle and share of the USA in the economic recovery; perceived linking of German economic development with other countries; attitude to a European Common Market; reasons for the high American standard of living; comparison between the USA and the FRG regarding working conditions, productivity, social security and job security of workers; image of Americans; knowledge of economic data of the USA; investment inclination; attitude to the competitive economy; assumed ownership of various branches of the economy in the FRG and in the USA, differences according to government and private; expected influence of the American government on the economy and vice versa; estimated proportion of members of the middle classes; image of American agriculture; judgement on the ideological influence of the USA on the FRG; sources of information about America; membership in clubs and organizations and offices taken on; party preference; self-assessment of social class; local residency. Demography: age (classified); marital status; religious denomination; school education; occupation; employment; household income; state; refugee status. Interviewer rating: social class and willingness of respondent to cooperate; number of contact attempts. Also encoded were: age of interviewer and sex of interviewer; city size. Beurteilung der wirtschaftlichen und sozialen Verhältnisse in den USA im Vergleich zur BRD. Themen: Entwicklung der persönlichen wirtschaftlichen Verhältnisse und des Lebensstandards in der BRD; Gründe für das sogenannte Wirtschaftswunder und Anteil der USA am wirtschaftlichen Aufschwung; wahrgenommene Verknüpfung der deutschen Wirtschaftsentwicklung mit anderen Ländern; Einstellung zu einem europäischen gemeinsamen Markt; Gründe für den hohen amerikanischen Lebensstandard; Vergleich zwischen USA und BRD bezüglich der Arbeitsbedingungen, Produktivität, Leistungsfähigkeit, Sozialversicherung und Arbeitsplatzsicherheit von Arbeitern; Image von Amerikanern; Kenntnis wirtschaftlicher Daten der USA; Investitionsneigung; Einstellung zur Wettbewerbswirtschaft; vermutete Eignerschaft verschiedener Wirtschaftszweig in der BRD und in den USA, unterschieden nach staatlich und privat; vermuteter Einfluß der amerikanischen Regierung auf die Wirtschaft und umgekehrt; geschätzter Anteil von Zugehörigen zum Mittelstand; Image der amerikanischen Landwirtschaft; Beurteilung des ideologischen Einflusses der USA auf die BRD; Informationsquellen über Amerika; Mitgliedschaft in Vereinen und Organisationen und übernommene Ämter; Parteipräferenz; Selbsteinschätzung der Schichtzugehörigkeit; Ortsansässigkeit. Demographie: Alter (klassiert); Familienstand; Konfession; Schulbildung; Beruf; Berufstätigkeit; Haushaltseinkommen; Bundesland; Flüchtlingsstatus. Interviewerrating: Schichtzugehörigkeit und Kooperationsbereitschaft des Befragten; Anzahl der Kontaktversuche. Zusätzlich verkodet wurden: Intervieweralter und Interviewergeschlecht; Ortsgröße.

Number of employees working overtime (weekly) and average overtime hours by North American Industry Classification System (NAICS), gender and age group, last 5 years.

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.

Percentage of workforce teleworking or working remotely prior to February 1, 2020, on May 29, 2020, during the COVID-19 pandemic, and percentage of workforce expected to continue teleworking or working remotely after the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.