In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Pursuant to Section 2-573 of the Cook County Code of Ethics, officials that hold employment outside their elected office shall disclose such employment, or any change in employment to the Ethics Director and the Board of Ethics within 30 days of engaging in such employment or change in employment. Such disclosures by officials should be posted and made publicly available on the Ethics Departments web-page.

United States Employment: NF: sa: Mfg: Sheet Metal Work data was reported at 109.500 Person th in May 2018. This stayed constant from the previous number of 109.500 Person th for Apr 2018. United States Employment: NF: sa: Mfg: Sheet Metal Work data is updated monthly, averaging 100.900 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 115.700 Person th in Nov 2000 and a record low of 78.500 Person th in Mar 1992. United States Employment: NF: sa: Mfg: Sheet Metal Work data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

This dataset presents a dual-version representation of employment-related data from India, crafted to highlight the importance of data cleaning and transformation in any real-world data science or analytics project.

🔹 Dataset Composition:

It includes two parallel datasets: 1. Messy Dataset (Raw) – Represents a typical unprocessed dataset often encountered in data collection from surveys, databases, or manual entries. 2. Cleaned Dataset – This version demonstrates how proper data preprocessing can significantly enhance the quality and usability of data for analytical and visualization purposes.

Each record captures multiple attributes related to individuals in the Indian job market, including: - Age Group
- Employment Status (Employed/Unemployed)
- Monthly Salary (INR)
- Education Level
- Industry Sector
- Years of Experience
- Location
- Perceived AI Risk
- Date of Data Recording

Transformations & Cleaning Applied:

The raw dataset underwent comprehensive transformations to convert it into its clean, analysis-ready form: - Missing Values: Identified and handled using either row elimination (where critical data was missing) or imputation techniques. - Duplicate Records: Identified using row comparison and removed to prevent analytical skew. - Inconsistent Formatting: Unified inconsistent naming in columns (like 'monthly_salary_(inr)' → 'Monthly Salary (INR)'), capitalization, and string spacing. - Incorrect Data Types: Converted columns like salary from string/object to float for numerical analysis. - Outliers: Detected and handled based on domain logic and distribution analysis. - Categorization: Converted numeric ages into grouped age categories for comparative analysis. - Standardization: Uniform labels for employment status, industry names, education, and AI risk levels were applied for visualization clarity.

Purpose & Utility:

This dataset is ideal for learners and professionals who want to understand: - The impact of messy data on visualization and insights - How transformation steps can dramatically improve data interpretation - Practical examples of preprocessing techniques before feeding into ML models or BI tools

It's also useful for: - Training ML models with clean inputs
- Data storytelling with visual clarity
- Demonstrating reproducibility in data cleaning pipelines

By examining both the messy and clean datasets, users gain a deeper appreciation for why “garbage in, garbage out” rings true in the world of data science.

United States Employment: NF: sa: Mfg: Plastic Bottle&LaminatedPlasticPlate,Sheet data was reported at 53.000 Person th in May 2018. This records an increase from the previous number of 52.400 Person th for Apr 2018. United States Employment: NF: sa: Mfg: Plastic Bottle&LaminatedPlasticPlate,Sheet data is updated monthly, averaging 53.400 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 66.000 Person th in Apr 2000 and a record low of 46.600 Person th in Jan 2013. United States Employment: NF: sa: Mfg: Plastic Bottle&LaminatedPlasticPlate,Sheet data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

Graph and download economic data for Employment for Manufacturing: Sheet Metal Work (NAICS 332322) in the United States (IPUEN332322W201000000) from 1988 to 2024 about NAICS, metals, IP, manufacturing, employment, and USA.

This dataset contains required veteran employment data by federal contractors/subcontractors, who are required to report annually the number of employees in their workforces who are veterans covered under VEVRAA. The U.S. Department of Labor's Veterans' Employment and Training Service (VETS) and Office of Federal Contractor Compliance Programs (OFCCP) have supported actions to employ and advance the employment of covered veterans since 2008. As legislatively mandated under 38 U.S. Code Section 4212, codified at 41 CFR 61-300, contractors and subcontractors who enter into, or modify a contract or subcontract with the federal government, and whose contract meets the criteria set forth in the above legislation / regulations, are required to report annually on their affirmative action efforts in employing veterans. Data reported through form VETS-4212 is used by OFCCP in compliance evaluations. Data are updated weekly (every Friday at 11PM ET).

Graph and download economic data for Employment for Manufacturing: Plastics Packaging Materials and Unlaminated Film and Sheet Manufacturing (NAICS 32611) in the United States (IPUEN32611W200000000) from 1987 to 2021 about plastics, materials, NAICS, IP, manufacturing, employment, and USA.

Provide the county and city with the annual report on the promotion of employment for people with disabilities.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's labour force characteristics have been removed, as well as eight out of date variables which have been removed from the record layout.

Employment situation of people with disabilities in this organization

1. CONTENTS OF THE TABLE This table shows the employed population in the Netherlands, Belgium, Lower Saxony and North Rhine-Westphalia according to the form of employment for different regional levels. The population aged 15 to under 75 is divided according to their participation in working life into the economically active (employed plus unemployed) and the economically inactive. A breakdown by type of employment, gender, age and level of qualification/education is available for the group of employed persons. The labor force concept of the International Labor Organization (ILO) is used to determine employment status. The ILO concept enables the temporal development of the extent of unemployment to be observed independently of the respective changes in social legislation. This information was compiled as part of the two Interreg programs Germany-Netherlands and Flanders-Netherlands to provide cross-border labor market data. Data available from: 2010 Status of the figures: The figures in this table are final. Changes as of 22 June 2021: Added Dutch and Belgian dates for 2020. The age group 20 – 65 years has been added. Changes as of 23 October 2020: Dates for 2019 have been added. Region GR322 deleted. Changes as of 1 May 2020: (Changes as of May 1, 2020:) The nationality attribute was removed from the table. Added solo self-employed, self-employed with employees, and family workers categories. The Dutch data for the variable education level were recalculated. The reason for the recalculation is an improvement in the methodology used to determine the level of education. Individuals who have acquired a qualification abroad that is comparable to higher vocational training (hbo) were classified in the intermediate level of education (mbo2) category. This has now been corrected. On the Belgian side, the data for the NUTS 3 region Verviers district - German-speaking community has been added. Changes as of 12 March 2020: Added the figures for the countries Netherlands and Belgium. Changes as of 20 September 2019: None as this is a new table. When will new figures be published? (When will new figures be released?) New figures will be released in the third quarter of 2021.

Apply to the labor insurance and employment insurance unit for reissuance of insurance premium payment slip.

A dynamic stochastic occupational choice model with heterogeneous agents is developed to evaluate the impact of a corporate income tax reduction on employment. In this framework, the key margin is the endogenous entrepreneurial choice of legal form of organization. A reduction in the corporate income tax burden encourages adoption of the C corporation legal form, which reduces capital constraints on firms. Improved capital reallocation increases overall productive efficiency in the economy and therefore expands the labor market. Relative to the benchmark economy, a corporate income tax cut can reduce the non-employment rate by up to 7 percent.

Release Date: 2024-06-27.Release Schedule:.The County Business Patterns (CBP) data, including ZIP Code Business Patterns (ZBP) data, in this file were released on June 27, 2024...Key Table Information:.Beginning with reference year 2007, CBP and ZBP data are released using the Noise disclosure methodology to protect confidentiality. See Program Methodology for complete information on the coverage and methodology of the CBP and ZBP data series..Includes only establishments with payrolls...Four employment-size classes (1,000 to 1,499 employees, 1,500 to 2,499 employees, 2,500 to 4,999 employees, and 5,000 or more employees) are only available at the CSA, MSA, and county-levels...ZBP data by employment size class, shown at the 2-6 digit NAICS code levels only contains data on the number of establishments. ZBP data shown for NAICS code 00 (Total for all sectors) contains data on the number of establishments, total employment, first quarter payroll, and annual payroll...For additional details regarding Congressional Districts, please see Program Methodology...Data Items and Other Identifying Records:.This table contains data classified by Legal Form of Organization (U.S. and state level only) and employment size category of the establishment..Number of establishments.Annual payroll ($1,000).First-quarter payroll ($1,000).Number of employees during the pay period including March 12.Noise range for annual payroll, first-quarter payroll, and number of employees during the pay period including March 12..Geography Coverage:.The data are shown at the U.S., State, County, Metropolitan/ Micropolitan Statistical Areas, Combined Statistical Areas, 5-digit ZIP code, and Congressional District levels. Also available are data for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) at the state and county equivalent levels...Industry Coverage:.The data are shown at the 2- through 6- digit NAICS code levels for all sectors with published data, and for NAICS code 00 (Total for all sectors)...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/cbp/data/2022/CB2200CBP.zip..API Information:.County Business Patterns (CBP) data are housed in the County Business Patterns (CBP) API. For more information, see CBP and ZBP APIs...Methodology:.In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The data are subject to nonsampling error such as errors of self-classification, as well as errors of response, nonreporting and coverage. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only.. .To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. For detailed information about the methods used to collect and produce statistics, see Program Methodology..Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals (used prior to 2017).G - Low noise; cell value was changed by less than 2 percent by the application of noise.H - Moderate noise; cell value was changed by 2 percent or more but less than 5 percent by the application of noise.J - High noise; cell value was changed by 5 percent or more by the application of noise.N - Not available or not comparable.S - Withheld because estimates did not meet publication standards.X - Not applicable.r - Revised (represented as superscript).For a complete list of symbols, see County Business Patterns Glossary...Source:.U.S. Census Bureau, 2022 County Business Patterns..For more information about County Business Patterns, see the County Business Patterns website...Contact Information:.U.S. Census Bureau.Economy-Wide Statistics Division.Business Statistics Branch.(301)763-2580.ewd.county.business.patterns@census.gov

Records used to verify employment eligibility through the I-9 form and any supporting documentation.

Country Wise Detail of Peoples who Gone for Foreign Employment form 2050-2051 to 2072-2073. Data is retrieved from Department of Foreign Employment.

County Business Patterns provides data covering most of the economic divisions of the economy, i.e., agricultural services, mining, construction, manufacturing, transportation, public utilities, wholesale trade, retail trade, finance, insurance, real estate, and services. Summary data are provided on number of employees for the mid-March pay period, first-quarter total payroll, total annual payroll, number of establishments, and the number of establishments by employment-size class. Data are tabulated by detailed industry based on the 1987 edition of the Standard Industrial Classification (SIC) manual. The employment data obtained from administrative records of the Internal Revenue Service since 1978 differ somewhat from previously published employment data, in that all persons employed by an establishment filing a Treasury Form 941 are included in the County Business Patterns program. Although Form 941 is used only by employers covered (at least partially) by FICA, the data also include individual employees of the same establishment who are themselves not covered by FICA. In past years, employees of this type were not included. The difference in employment coverage will affect only certain types of activities, notably those where retirement systems other than Social Security are provided, or where part-time employees are prevalent. Examples would include hospitals and educational institutions. Administrative data for employees of establishments totally exempt from FlCA are excluded as are data for self-employed persons. The file provides data on total number of establishments, mid-March employment, first quarter and annual payroll, and number of establishments by employment-size classes. Whereas data in County Business Patterns reports are not shown for SIC's with fewer than 50 employees in a given area, there is no such restriction on the data files. This series excludes governmental establishments classified in the covered industries except for liquor stores and wholesale liquor establishments operated by State and local governments. All government hospitals are included beginning with 1989 data. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection. NOTES.TIME

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's labour force characteristics have been removed, as well as eight out of date variables which have been removed from the record layout.