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.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May 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.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

----> Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

This statistic represents the percentage change in manufacturing employment in selected countries worldwide between 1997 and 2016. In that time span, France's manufacturing employment decreased by approximately 24 percent.

In October 2024, about 133.5 million people in the United States were employed on a full-time basis. In line with the definition of the BLS, full-time workers are persons who usually work 35 hours or more per week. Seasonal adjustment is a statistical method for removing the seasonal component of a time series used when analyzing non-seasonal trends, whereas non-seasonally-adjusted reflects the actual current data.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Jun 2025 about headline figure, establishment survey, manufacturing, employment, and USA.

Abstract

Bangladesh Bureau of Statistics (BBS) has initiated the labor force survey on a quarterly basis, to measure the levels and trends of employment, unemployment and labor force in the country on a continuous basis. In the past, labor force surveys conducted at four-five yearly time intervals since 1980.

Detailed information on labor force characteristics has been collected from representative sample of 123 thousand households to produce gender disaggregated national and divisional level estimates with urban/rural/city corporation breakdown. The survey also provides quarterly representative results and sample size for each quarter was 30,816 households. The survey, along with the quantification of core variables, also estimates important attributes of literacy, migration, own use production of goods and own use provision of services, volunteer work, occupational safety and health etc. The estimates are profiled according to latest classifications viz Bangladesh Standard Industrial Classification (BSIC 2009 based on ISIC rev-4) and Bangladesh Standard Classification of Occupations (BSCO- 2012 in line with ISCO-2008).

The primary objective of the survey was to collect comprehensive data on the Labor Force, employment and unemployment of the population aged 15 or older for use by the Government, international organizations, NGOs, researchers and others to efficiently provide targeted interventions. Specific objectives of the survey: - Provide relevant information regarding the characteristics of the population and household that relate to housing, household size, female-headed households; - Provide detailed information on education and training, such as literacy, educational attainment and vocational training; - Provide relevant information on economic activities and the labor force regarding the working-age population, economic activity status and Labor Force participation; - Provide detailed information on employment and informal employment by occupation and industry, education level and status in employment; - Provide relevant information on unemployment, the youth labor force participation, youth employment, and youth unemployment; - Provide other information on decent work regarding earnings from employment, working hours and time-related underemployment, quality and stability of employment, social security coverage, and safety at work, equal opportunities; - Provide relevant information on non-economic activities, volunteer activities etc.

Geographic coverage

National coverage

Analysis unit

• Individual
• Household

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The quarterly Labor Force Survey 2016-17 questionnaire comprised of 14 sections:

Section 1. Household basic information Section 2. Household roster (members' basic information) Section 3. General education (for persons aged 5 years or older) & vocational training (for persons aged 15 years or older) Section 4. Working status (for persons aged 15 years or older) Section 5. Main activities (for persons aged 15 years or older) Section 6. Secondary activities (for employed persons aged 15 years or older) Section 7. Occupational safety and health within the previous 12 months (for persons aged 15 years or older) Section 8. Time-related underemployment (for employed persons aged 15 years or older) Section 9. Unemployment (for not employed persons aged 15 years or older) Section 10. Own use production of goods (for persons aged 15 years or older) Section 11. Own use provision of services (for persons aged 15 years or older) Section 12. Unpaid trainee/apprentice work (for persons aged 15 years or older) Section 13. Volunteer work (for persons aged 15 years or older) Section 14. Migration (for persons aged 15 years or older)

Cleaning operations

Editing and processing errors, several consistency checks were done, both manually and computerized program using CSPro; batch editing was done using Stata, to ensure the quality and acceptability of the data produced. The non-sampling error is to ensure high quality data, several steps were taken to minimize non-sampling errors. Unlike sampling errors, these errors cannot be measured and can only be overcome through several administrative procedures. These errors can arise as a result of incomplete survey coverage, frame defect, response error, non-response and processing errors such as during editing, coding and data capture.

Sampling error estimates

Sampling error is a result of estimating data based on a probability sampling, not on census. Such error in statistics is termed as relative standard error and often denoted as RSE which is given in percentage. This error is an indication to the precision of the parameter under study. In other words, it reflects the extent of variation with other sample-based estimates. Sampling errors of estimates on a few important variables at national levels are calculated separately as shown in the annex. For example, the labor force participation rate at the national level was 67.0 per cent with an RSE of 0.23 per cent and standard error (SE) of 0.16 per cent. At 95 per cent confidence interval (a = 0.05), the labor force participation rate was in the range of 66.69-67.31 per cent.

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

In May 2024, about 4.5 percent of recent college graduates were unemployed in the United States. This was a significant decrease from September 2020, when the unemployment rate among recent college graduates was at nine percent.

Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

The Ministry of Finance (MoF) has published helpful Labour and Employment information from the Central Statistical Office (CSO) that can be accessed via shared links. The Labour and Employment dataset links are based on the following: Change in Productivity and Average Weekly Earnings /Percentage Change/ for 2016 to 2021 Population, Labour Force and Employment (Mid-year) for 2016 to 2022 Mid-year Estimates of Population by Age for 2016 to 2022 Labour Force by Industry and Employment Status (CSSP Estimates) /Hundreds (‘00)/ for 2020 to 2022

Abstract

On 17 December 2015, the General Director of General Statistic Office issued Decision No 1160/QD-TCTK on the 2016 Labour Force survey, along with its survey plan. The purpose of the survey was to collect the information on 2016 labor market participation from those people who are 15 years old and above currently residing in Vietnam; regarded as a basic for aggregating and compiling national statistical indicators on labor, employment, unemployment and income. These results would support for ministries and branches assessing and comparing the changes in labour market among quarters within the reference year as well as with those of previous annual labour force surveys conducted by GSO. These results would be also considered as a basic to develop and plan policies on human resource development; activities of investment, production and business accordant with the development trend on labor market; as well as to access and apply International Labor Organization’s updated recommendations on labor and employment, especially in term of “labor under-utilization” into the reality of Vietnam. The statistics would be aggregated quarterly for the national and regional levels; and yearly for the provincial level.

Geographic coverage

Whole country.

Analysis unit

• Households
• Individuals ages 15 and above

Universe

Population ages 15 and over (working age population).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling frame The sample of 2016 Labor Force Survey is the 2 stage stratified sample in order to ensure the presentative of quarterly aggregated statistics for the whole country, urban/rural, 6 social economic regions, Hanoi and Hochiminh cities as well as annually aggregated ones for 63 provinces/cities. Each province/city would constitute a main stratum with two sub-stratums namely urban and rural ones. The sampling frame is based on the 2015 Inter-censal Population and Housing Survey's selected enumeration areas.

Sample size The 2016 Labor Force Survey was conducted with the sample size of 50.640 households/quarter, (that is, equivalent to 16.880 households/month). The sample size was designed and allocated to ensure the statistical significance/ preventative of quarterly aggregated statistics at regional level and annually aggregated ones at provincial level.

Sampling deviation

The sample of this survey is stratified into 2 stages and designed as follows:

• Stage 1 (selecting EAs): Each province/city will constitute a main stratum divided into 2 sub stratums (of which, one will be representative for urban areas and the other is for rural areas). At this stage, list of provincial enumeration areas (the master sample frame – taken from the 1/4/2014 Inter-censal Population and Housing Survey’s 20% sample) will be divided into 2 independent sub-sample frames (urban and rural), and EAs will be selected by the method of probability proportional to size - PPS.

• Stage 2 (selecting households): At each selected EA (that is determined in stage 1), after updating the EA and making the list of households, the updated list of households will be divided into 2 groups (defined as the upper/first and the lower/ second half of the list of households). Then, at each half, 15 households will be selected systematically.

In order to improve the design efficiency and ensure to the reliability of survey sample, the sample will be selected alternately (under the 2-2-2 rotation). By this way, each EA will be divided into 02 rotational groups, whose households will be selected into sample in two adjacent quarters, and then excluded in 2 succeeding adjacent quarters, finally selected again into the sample in 2 following adjacent quarters. Each EA will be selected into the sample 4 times during a year at most.

Mode of data collection

Face-to-face [f2f]

Research instrument

Single questionnaire covering: - Household characteristcs - Individual characterists for those ages 15 and over as well as information on economic activity or inactivity

Response rate

Residence/Socio-economic region Total Male Female Labor force participation rate Entire country 100.0 100.0 100.0 77.5 Urban 31.9 32.0 31.9 71.0 Rural 68.1 68.0 68.1 81.0

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.

Employment and unemployment data by city for places in San Mateo County. CDP is "Census Designated Place" - a recognized community that was unincorporated at the time of the 2000 Census.

1) Data may not add due to rounding. All unemployment rates shown are calculated on unrounded data. 2) These data are not seasonally adjusted.

Methodology: Monthly city and CDP labor force data are derived by multiplying current estimates of county employment and unemployment by the employment and unemployment shares (ratios) of each city and CDP at the time of the 2000 Census. Ratios for cities of 25,000 or more persons were developed from special tabulations based on household population only from the Bureau of Labor Statistics. For smaller cities and CDP, ratios were calculated from published census data.

City and CDP unrounded employment and unemployment are summed to get the labor force. The unemployment rate is calculated by dividing unemployment by the labor force. Then the labor force, employment, and unemployment are rounded.

This method assumes that the rates of change in employment and unemployment, since 2000, are exactly the same in each city and CDP as at the county level (i.e., that the shares are still accurate). If this assumption is not true for a specific city or CDP, then the estimates for that area may not represent the current economic conditions. Since this assumption is untested, caution should be employed when using these data.

United States Employment: NF: Mfg: Cutlery & Hand Tool data was reported at 43.200 Person th in Sep 2018. This stayed constant from the previous number of 43.200 Person th for Aug 2018. United States Employment: NF: Mfg: Cutlery & Hand Tool data is updated monthly, averaging 58.800 Person th from Jan 1990 to Sep 2018, with 345 observations. The data reached an all-time high of 80.400 Person th in Jun 1998 and a record low of 37.100 Person th in Aug 2016. United States Employment: NF: Mfg: Cutlery & Hand Tool 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.G024: Current Employment Statistics Survey: Employment: Non Farm.

As of October 2024, there were 133.89 million full-time employees in the United States. This is a slight decrease from the previous month, when there were 134.15 million full-time employees. The impact COVID-19 on employment In December 2019, the COVID-19 virus began its spread across the globe. Since being classified as a pandemic, the virus caused a global health crisis that has taken the lives of millions of people worldwide. The COVID-19 pandemic changed many facets of society, most significantly, the economy. In the first years, many businesses across all industries were forced to shut down, with large numbers of employees being laid off. The economy continued its recovery in 2022 with the nationwide unemployment rate returning to a more normal 3.4 percent as of April 2023. Unemployment benefits Because so many people in the United States lost their jobs, record numbers of individuals applied for unemployment insurance for the first time. As an early response to this nation-wide upheaval, the government issued relief checks and extended the benefits paid by unemployment insurance. In May 2020, the amount of unemployment insurance benefits paid rose to 23.73 billion U.S. dollars. As of December 2022, this value had declined to 2.24 billion U.S. dollars.

Educational status and labour market status of people aged 16 to 24 years, by sex, in and out of full-time education, UK, rolling three-monthly figures published monthly, seasonally adjusted. Labour Force Survey. These are official statistics in development.

The latest National Statistics on forestry produced by the Forestry Commission were released on 22 September 2016 according to the arrangements approved by the UK Statistics Authority.

Detailed statistics are published in the web publication Forestry Statistics 2016, with an extract in Forestry Facts & Figures 2016. They include UK statistics on woodland area, planting, timber, trade, climate change, environment, recreation, employment and finance & prices as well as some statistics on international forestry. Where possible, figures are also provided for England, Wales, Scotland and Northern Ireland.

This dataset covers statistics on employment in forestry and wood processing, health and safety and businesses.

In the busy last quarter of the year, Target announced around 100,000 open retail positions to tackle the increased demand of the holday season period in 2024. Target's seasonal hirings have been stable in the last four years.