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

This file provides data for October 1982 drawn from the 1982 Census of Governments on full- and part-time employment, full-time equivalent employment, and payroll statistics by type of government (State, county, township, special district, and school district) and by function (elementary and secondary education, police protection, higher education, fire protection, financial administration, central administration, judicial and legal, highways, public welfare, sanitation other than sewerage, sewerage, parks and recreation, health, hospitals, water supply, electric power, gas supply, transit systems, natural resources, corrections, libraries, airports, water transportation, other education, State liquor stores, Employment Security Administration, and housing and urban renewal). Also shown are data on policies for labor-management relations, number of organized employees by function, number of bargaining units, number of employees in bargaining units, number of contractual agreements, and number of employees covered by contractual agreements. State and local data are provided for the 50 States and the Distrcit of Columbia.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08286.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Graph and download economic data for Layoffs and Discharges: Total Nonfarm (JTSLDR) from Dec 2000 to Jun 2025 about discharges, layoffs, nonfarm, and USA.

Labor Force Participation Rate in the United States decreased to 62.20 percent in July from 62.30 percent in June of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

PPI: Mining: EO: NM: SC: CC: PP: Fire Clay/Fuller's Earth & Feldspar data was reported at 156.600 Jun2007=100 in Dec 2018. This records an increase from the previous number of 147.400 Jun2007=100 for Nov 2018. PPI: Mining: EO: NM: SC: CC: PP: Fire Clay/Fuller's Earth & Feldspar data is updated monthly, averaging 140.600 Jun2007=100 from Jun 2007 (Median) to Dec 2018, with 137 observations. The data reached an all-time high of 167.500 Jun2007=100 in Aug 2017 and a record low of 99.700 Jun2007=100 in Jul 2007. PPI: Mining: EO: NM: SC: CC: PP: Fire Clay/Fuller's Earth & Feldspar 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.I081: Producer Price Index: by Industry: Logging and Mining.

Federal, state, and local government employment data are provided in this file. Full- and part-time employment, full-time equivalency, part-time employee hours worked, and payroll statistics are included. Data are supplied by type of government (federal, state, county, municipality, township, special district, and school district) and by function. Governmental functions include education (elementary, secondary, and higher education), police and fire protection, financial administration, judicial and legal functions, highways, solid waste management and sewage, libraries, air and water transportation and terminals, state liquor stores, social insurance administration, housing and community development, utilities, public welfare, parks and recreation, health care, transit, and natural resources. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06004.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

We present a dataset created from merged secondary sources of ExecuComp and CompuStat and then augmented with manual data collection through searches of news stories related to CEO turnover.

We start dataset construction with the ExecuComp executive-level data for the period from 1992 through 2020. These data are merged with the CompuStat dataset of financial variables. As the dataset is intended for research on CEO turnover, we exclude observations in which the CEO at the start of the fiscal year is not well-defined; these are cases when there were co-CEOs and cases when the CEO was shared across different firms. The data set also excludes firm/year combinations that involve a restructuring of the firm – spinoff, buyout, merger, or bankruptcy.

We identify the CEO at the start of each year for each firm. This also helps identify the last year an individual served as CEO. In order to identify CEO turnover based on changes in the CEO from year to year, we require firm observations to extend over at least six contiguous years for the firm to remain in the sample. Cases involving the last year the firm is in the sample are excluded. We also exclude from the dataset cases when there was an interim CEO who stayed in the position for less than 2 years. This results in a sample of 3,100 firms reflecting 41,773 firm/year combinations.

For this sample, we examine news articles related to CEO turnover to confirm the reasons for each CEO departure case. We use the ProQuest full-text news database and search for the company name, the executive name, and the departure year. We identify news articles mentioning the turnover case and then classify the explanation of each CEO departure case into one of five categories of turnover. These categories represent CEOs who resigned, were fired, retired, left due to illness or death, and those who left the position but stayed with the firm in a change of duties, respectively.

The published data file does not include proprietary data from ExecuComp and CompuStat such as executive names and firm financial data. These data fields may be merged with the current data file using the provided ExecuComp and CompuStat identifiers.

The dataset consists of a single table containing the following fields: • gvkey – unique identifier for the firms retrieved from CompuStat database • firmid – unique firm identifier to distinguish distinct contiguous time periods created by breaks in a firm’s presence in the dataset • coname – company name as listed in the CompuStat database • execid – unique identifier for the executives retrieved from ExecuComp database • year – fiscal year • reason – reason for the eventual departure of the CEO executive from the firm, this field is blank for executives who did not leave the firm during the sample period • ceo_departure – dummy variable that equals 1 if the executive left the firm in the fiscal year, and 0 otherwise

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

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)

United States CES: AAE: Housing: Shelter: Owned: Maintenance, Repairs, Ins & Other data was reported at 1,616.000 USD in 2017. This records an increase from the previous number of 1,437.000 USD for 2016. United States CES: AAE: Housing: Shelter: Owned: Maintenance, Repairs, Ins & Other data is updated yearly, averaging 869.000 USD from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 1,616.000 USD in 2017 and a record low of 390.000 USD in 1984. United States CES: AAE: Housing: Shelter: Owned: Maintenance, Repairs, Ins & Other data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.H042: Consumer Expenditure Survey. CES: AAE: Housing: Shelter: Owned: Maintenance, Repairs, Insurance & Other Expenses (id 292780401) are owner-occupied shelter expenditures that includes refinancing and prepayment charges, ground rent, expenses for property management and security, homeowners' insurance, fire insurance and extended coverage, expenses for repairs and maintenance contracted out, and expenses of materials for owner-performed repairs and maintenance for dwellings used or maintained by the consumer unit. Maintenance and Repairs included in this series pertain to residential units only and not to electronics/kitchen equipment.

In February 2025, the number of job losers and persons who completed temporary jobs in the United States stood at about 3.3 million and is used when analyzing non-seasonal trends. The monthly unemployment rate can be found here.

The Employment Summary Statistics dataset is part of the Census of Governments, a complete enumeration of United States governmental units undertaken every five years. This data collection contains the October, 1982 employment and payroll figures for the governments. Data for full- and part-time employment and payrolls are shown for such functions as administration, education, corrections, police, fire protection, utilities, health, public welfare, parks, libraries, sanitation, highways, and transit. Data are also provided for labor-management relations, employee organizations, employee benefits, and unemployment, health, and life insurance. There are four files in this collection. File A provides detailed statistics for each state and local government, File B has the data for local governmental units aggregated by county, and File C has national and state summaries for the following types of governments: (1) State and Local Government Total, (2) State Government, (3) Local Government, (4) Local Governments in SMSA's, (5) Counties, (6) Municipalities, (7) Townships, (8) School Districts, and (9) Special Districts. In addition, the Name and Address File contains name, address, and corresponding government identification code for all of the local governmental units. (Source: ICPSR, retrieved 06/16/2011)

As of January 2024, the tech startup with the most layoffs was Amazon, with over 27 thousand layoffs, across five separate rounds of layoffs. It was followed by Meta and Google with around 21 thousand and 12 thousand job cuts announced respectively.

Layoffs in in the technology industry

Overall, layoffs across all industries began in 2020 due to the outbreak of the coronavirus (COVID-19) pandemic, with tech layoffs increasing in 2022. In the first quarter of 2023 alone, more than 167 thousand employees had been fired worldwide, a record number of job cuts in a single quarter and more than all of the layoffs announced in 2022 combined, marking a harsh start to of 2023 for the tech sector. From retail to finance and education, all sectors are suffering from this widespread downsizing. However, retail tech startups were hit the most, with almost 29 thousand layoffs announced as of September 2023. Most job losses happened in the United States, where tech giants like Amazon, Meta, and Google are based.

Reasons behind increasing tech layoffs

Layoffs in the technology sector started with the COVID-19 pandemic in 2020 when entire cities were in lockdown and mobility was restricted. Although restrictions loosened up in 2021, events such as the Russia-Ukraine war, the downturn in Chinese production, and rising inflation had a significant impact on the tech industry and continue to represent major concerns for tech companies. As a consequence, companies across the world have yet to overcome all economic challenges, examples of which are rising material and labor costs, as well as decreasing profit margins. To address such difficulties, tech companies have appointed business plans. For instance, in the United States, tech firms planned to focus more on consumer retention, automating software, and cutting operating expenses.

This dataset table shows personnel employed by the Fire and Rescue Service in Wales (headcounts) by age, gender and employment type.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Angel Fire. The dataset can be utilized to gain insights into gender-based income distribution within the Angel Fire population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Angel Fire, among individuals aged 15 years and older with income, there were 433 men and 487 women in the workforce. Among them, 99 men were engaged in full-time, year-round employment, while 75 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while 9.33% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 26.26% of men in full-time roles earned incomes exceeding $100,000, while 22.67% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Angel Fire median household income by race. You can refer the same here

Abstract

Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labour force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Statistics on the labour force further deals with the measurement of economic activity status and its relationship between other social and economic characteristics of the population. Seasonal and other variations as well as changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys. It is also an input for assessing the meeting of the Millennium Development Goals (MDGs). Furthermore, data on economic activity and other labour force data would be used as a springboard for monitoring and evaluation of the five years growth and transformation plan that launched at different levels of the country.

In order to fill the gap in data requirement for the purpose of socio-economic development planning, monitoring and evaluation, the Central Statistical Agency (CSA) has been providing labour force and related data at different levels with various contents and details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys (RLFS). Also, the 1984, 1994 and 2007 Population and Housing Censuses and the 1999 and 2005 National Labour Force Surveys provided a comprehensive national labour force data representing both urban and rural areas.

The survey results mainly provide data on the main characteristics of employed and unemployed, that is, the work force engaged or available to be engaged in the production of economic goods and services and its distribution in the various sectors of the economy during a given reference period.

To capture child labour data, the former minimum age limit 10 years was lower down to 5 years during the survey periods May 2009 and May 2010. Therefore, the data in these surveys was collected from those persons aged five years and over. However, for the purpose of measuring the economic activity status based on Ethiopian situation the lower age limit was fixed at ten years. This is because children in rural and urban areas used to work at their early age such as collection of fire wood, looking after cattle, shoeshine, street vendor, petty trading…etc. Therefore, the May 2010 Urban Employment and Unemployment Survey statistical report is mainly aimed at providing information on the economic characteristics of the population aged ten years and over.

In addition, a separate section that deals with filtering question for informal employment sector were also attached to the main questionnaire as a module. Except this section, almost similar questions that were used for the fourth round are administered in this series.

Geographic coverage

The 2010 Urban Employment and Unemployment Survey (UEUS) covered all urban parts of the country except three zones of Afar, Six zones of Somali, where the residents are pastoralists.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

This survey follows household approach and covers households residing in conventional households and thus, population residing in the collective quarters such as universities/colleges, hotel/hostel, monasteries and homeless population etc., are not covered by this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME The list of households obtained from the 2007 population and housing census is used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame in order to select households from sample EAs.

SAMPLE DESIGN For the purpose of the survey the country was divided into two broad categories. That is major urban center and other urban center categories.

Category I:- Major urban centers:- In this category all regional capitals and four other major urban centers that have a high population size as compared to others were included. Each urban center in this category was considered as a reporting level. The category has a total of 15 reporting levels. In this category, in order to select the sample, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs of each reporting level. From each sample EA 30 households were then selected as a Second Stage Unit (SSU).

Category II:- Other urban centers: Urban centers in the country other than those under category I were grouped into this category. A domain of other urban centers is formed for each region. Consequently 8 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category I. Hence, no domain was formed for these regions under this category.

A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. From each EA 30 households were finally selected at the third stage and the survey questionnaires administered for all of them.

SAMPLE SIZE AND SELECTION SCHEME Category I: - In this category 394 EAs and 11,820 households were selected. Sample EAs from each reporting level in this category were selected using probability proportional to size systematic sampling; size being number of households obtained from the 2007 population and housing census. From the fresh list of households prepared at the beginning of the survey, 30 households per EA were systematically selected and covered by the study.

Category II:- 81 urban centers, 266 EAs and 7,980 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic method; size being number of households obtained from the 2007 Population and housing census. From the listing of each EA then 30 households were systematically selected and the study performed on them.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire is organized into six sections; Section - 1: Area identification of the selected household: this section deals with area identification of respondents such as region, zone, wereda, etc. Section - 2: Particulars of household members: it consists of the general socio-demographic characteristics of the population such as age, sex, educational status, types of training and marital status. Section - 3: Economic activity during the last seven days: this section deal with whether persons were engaged in productive activities or not during the last seven days prior to date of interview, the status and characteristics of employed persons such as occupation, industry, employment status, hours of work, employment sector /formal and informal employment/ and earnings from paid employment. Section - 4: Unemployment rate and characteristics of unemployed persons: this section focuses on the size, distribution and characteristics of the unemployed population and unemployment rate only for those aged 10 years and over. Section - 5: Economic activity during the last six months: this section contains information on the economic activity status of the population in the long reference period or during the last six months.
Section - 6: Economic activity of children aged 5-17 years: this section consists of information on the participation of children aged 5-17 years in the economic activities, whether attending education, reason for not attending education…etc.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject experts from Manpower Statistics Team of the CSA.

Response rate

It was initially planned to cover 660 EAs and 19800 households in the survey, but ultimately 100% of EAs and 99.7% of households were successfully covered.

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.

The average unemployment rate was six percent in Germany in 2024. Since 2005, the rate of unemployment has generally been declining, though a slight increase was evident in recent years. Unemployment in Germany and comparison with other countries Germany has a comparatively low unemployment rate compared to its European neighbors, and they are expected to stay at around three percent over the next few years. This is a result of the damage the economy suffered during the COVID-19 pandemic. During the lockdown, most businesses were closed, and many companies lost revenue meaning employees were let go. It is also possible that higher unemployment figures will continue into later years because of inflation and rising energy prices. There is also a slightly higher unemployment rate among men than there is among women. Social support Social support is money paid out to those who are unable to work for some reason, its purpose is to protect those who are most vulnerable. The status of being unemployed is defined as when an employed person is laid off, fired, or quits his work and is still looking for a job, this is what qualifies someone to receive a citizens allowance (Bürgergeld) in Germany. The payments are only made if you are unemployed and worked for the last 12 months. Otherwise, benefits are received in the form of Arbeitslosengeld II, also called Hartz IV, which distributes social payments to people without an income who cannot work to make a living. Since January 2023 though, Arbeitlosengeld has been replaced by Bürgergeld, since this is a new transition, it is still possible that people will still refer to the benefits as Arbeitlosengeld or Hartz IV.

This study contains the specific October 1977 employment and payroll figures by function. Full-time and part-time employment and payrolls are shown for the following functions: corrections, education, administration, fire protection, health, libraries, police protection, public welfare, utilities, parks, transit, sewerage, and highway maintenance. Data are also provided on labor-management relations, full-time employee benefits, health, hospital or disability insurance and life insurance.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08117.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.