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.

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.

This statistic shows the U.S. unemployment rate from 1971 to 2015, by income tier. In 2015, about 4 percent of U.S. adult residents that were part of the middle class, were unemployed.

This study defined middle class income households as those with an income between 67 and 200 percent of the U.S. median household income, after adjustment for household size. Middle class income ranges from about 42,000 U.S. dollars to about 126,000 U.S. dollars per year for a three-person household.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

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.

Mexico Labour Force: Employment Rate: Female data was reported at 96.615 % in Mar 2019. This records an increase from the previous number of 96.530 % for Feb 2019. Mexico Labour Force: Employment Rate: Female data is updated monthly, averaging 95.659 % from Jan 2005 (Median) to Mar 2019, with 171 observations. The data reached an all-time high of 97.164 % in Dec 2005 and a record low of 92.959 % in Aug 2009. Mexico Labour Force: Employment Rate: Female data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G011: Employment Rate: ENOE 2015: Age 15 and Above. Since May 2013, Unemployment Rate is not comparable to prior months due to the new population projection data from CONAPO are incorporated.

Explore the dataset on unemployment rates for Saudis and non-Saudis by sex in Saudi Arabia . Gain insights on labor trends and gender disparities with SAMA Annual data.

Labor, Unemployment, Nationality, Gender, SAMA Annual

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..

This statistic shows data on employment and labor force participation in Finland from August 2015 to August 2018. In August 2018, the total labor force participation rate was 66.8 percent. At the same time period, the unemployment rate for women from 15 to 74 years was 7.3 percent, while the corresponding figure for men amounted to 6.3 percent.

{"definition": "Percent of the civilian labor force 16 years and older that are unemployed", "availableYears": "2015", "name": "Unemployment rate, 2015", "units": "Percent", "shortName": "UnempRate2015", "geographicLevel": "County", "dataSources": "Bureau of Labor Statistics, Local Area Unemployment Statistics"}

© UnempRate2015 This layer is sourced from gis.ers.usda.gov.

Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Jun 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

Mexico Employment: Agriculture as % of Total Labour Force data was reported at 11.544 % in Mar 2019. This records a decrease from the previous number of 13.032 % for Feb 2019. Mexico Employment: Agriculture as % of Total Labour Force data is updated monthly, averaging 13.792 % from Jan 2005 (Median) to Mar 2019, with 171 observations. The data reached an all-time high of 16.217 % in Aug 2005 and a record low of 11.544 % in Mar 2019. Mexico Employment: Agriculture as % of Total Labour Force data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G011: Employment Rate: ENOE 2015: Age 15 and Above. Since May 2013, Unemployment Rate is not comparable to prior months due to the new population projection data from CONAPO are incorporated.

The employment rate and household consumption are two indicators that are directly related. In this statistic, the year-on-year employment and household consumption variation in Spain are compared across the fourth quarter from 2015 to 2023. In the last quarter of 2023, the YoY employment rate in Spain amounted to *** percent, while the household consumption rate indicated an inter-annual change of *** percent.

Abstract

Bangladesh Bureau of Statistics (BBS), the National Statistical Organization of the country, has been conducting Labour Force Survey (LFS) since 1980 and repeated it every three/four year until 2013. The surveys could not be held at uniform time intervals due to resource constraint and other reasons. Finally, from July 2015, BBS has undertaken a development project and started implementation of quarterly labour force survey to provide labour market indicators. Gender disaggregated data on labour force, employment, unemployment, underemployment, not in labour force, hours worked, earnings, informal employment. Non-economic activities, volunteer activities are available in this report. The survey found that around half (51.2 per cent) of the 30.5 million employed persons worked more than 48 hours per week. By sex, the proportion of male workers working more than 48 hours (60.9 per cent) was higher than that of female workers (28.4 per cent). By industry, the highest rates of persons in excessive hours were in the Accommodation and food service activities (78.4 per cent), wholesale and retail trade sector (72.9 per cent), manufacturing (69.3 per cent), and households (61.5 per cent).

The primary objective of the survey was to collect comprehensive data on the Labour 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 labour force regarding the working-age population, economic activity status and Labour 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 labour 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

• Individuals

• Household

Universe

Age is a strong determinant of labour market so a common age cut-off and categories are important. The labour related questions of the survey refer to the population of 15 years old and over. The following age ranges is used in presenting the statistics: 15–24; 25–34; 35–44; 45–54; 55–64; and 65 and over. Besides, LMI is provided separately for youths as the youths are more prone to unstable transition to labour market. However, in setting the minimum LFS coverage age is the fact that the Government of Bangladesh, being aware that many young people, who are unable to continue with higher schooling, enter the labour market instead, has set the legal age for admission to employment at 14 completed years. Given that, inclusion of persons aged 15 years and over may result in the undercount of persons employed or unemployed in the country.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used for the selection of sample for the survey was based on the Population and Housing Census 2011. Sampling Frame which was made up of preparing of PSUs that is consists of collapsing one or more Enumeration Area (EAs) that was created for the Population and Housing Census 2011. EAs is geographical contiguous areas of land with identifiable boundaries. On average, each PSUs has 225 households. All the Enumeration areas of the country was identified into three segments viz. Strong, Semi-strong and not-strong based on the housing materials. The frame has 1284 PSUs/EAs spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into twenty-one domains viz. Rural, Urban and City corporations of seven administrative divisions.

From each selected PSUs/EAs, an equal number of 24 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the HQ and assigned to the Enumerators, with strictly no allowance for replacement of non-responding households. The Bangladesh Quarterly Labor Force Survey (QLFS) sample will be selected in two stages, with small area units called Primary Sampling Units (PSUs) in the first stage and a cluster of 24 households per PSU in the second stage. Both stages are random selections. The survey will implement a rotational panel strategy, in which some of the households in each cluster will be replaced by new households each quarter. The survey launched in July 2015, with a total sample size of about 30,800 households (1,284 PSUs) in each quarter and 123634 in the year 2015-16, intended to deliver reliable quarterly estimates of unemployment and other relevant labor force indicators for of the country's seven divisions and locality viz. national level estimates with disaggregation by City Corporations, Rural and Urban.

The survey involved a sample of 30816 households from 1284 PSUs/sample enumeration areas distributed across all the 64 Districts for each quarter and the ultimate sample households for the year 2015-16 was 126000 in total. The survey covered both urban and rural areas and dwelling households, including one person households. The institutional households, that is, those living in hostels, hotels, hospitals, old homes, military and police barracks, prisons, welfare homes and other institutions were excluded from the coverage of the survey.

Sampling deviation

Most BBS household surveys use a two-stage sampling strategy similar to that of the QLFS, and most of them share a common set of PSUs – the Integrated Multi-Purpose Sample (IMPS) – as a basis for their first sampling stage. However, the QLFS, given the specificities of its rotational strategy, has opted for choosing an independent set of PSUs for this purpose. The first stage sample frame of the QLFS was developed on the basis of the list of Enumeration Areas (EAs) generated by the 2011 Census. Some of the original 293,093 EAs were deemed too small to support the adopted rotational panel strategy, and were joined to neighboring EAs in order to create 146,576 PSUs of more adequate size: most of the resulting PSUs have between 150 and 300 households, with an average of 217. Whenever possible, the EAs with less than 150 households were appended to EAs from the same village, although in the most sparsely populated areas it was sometimes necessary to append entire villages to neighboring villages within the same mauza or mahalla (the lower level administrative division of the country.)2 Entire mauzas or mahallas were never appended to neighboring areas, even if they were too small – they remained as individual PSUs in the sample frame. The second stage sample frame will be a full listing of all households in the selected PSUs. The listings were completed between February and March 2015. If the survey indeed becomes a regular exercise, they should be permanently updated so that they are never older than two years.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Quarterly Labour Force Survey 2015-16 questionnaire comprised 14 sections, as follows:

• 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. 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

With regard to editing and processing errors, several consistency checks were done, both manually and computerized programme 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

Unemployment Rate in Bolivia decreased to 2.50 percent in the fourth quarter of 2024 from 2.70 percent in the third quarter of 2024. This dataset provides - Bolivia Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The percent of persons between the ages of 16 and 64 that are in the labor force (and are looking for work) but are not currently working. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

Unemployment Rate in China remained unchanged at 5 percent in June. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Focuses mainly on labor force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside the labor force, labor force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates.

Analysis unit

• Household,
• Individual.

Universe

The survey covered all the Palestinian persons aged 10 years and above who are a usual residence in State of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a two-stage stratified cluster random sample. Stratification: Four levels of stratification were made: 1. Stratification by Governorates. 2. Stratification by type of locality which comprises: (a) Urban (b) Rural (c) Refugee Camps The estimated sample size in each quarter is 7,616 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Labor Force Survey questionnaire consists of four main sections:

1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.
2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.
3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.
4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

All questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

The response rate was 89.5% in 2015, and in quarters: First quarter 2015: 90.6% Second quarter 2015: 89.7% Third quarter 2015: 89.5% Fourth quarter 2015: 88.4%

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report

Seasonally Adjusted Unemployment Rates Salt Lake County 1990-2015. Seasonal adjustment is a statistical technique that attempts to measure and remove the influences of predictable seasonal patterns to reveal how employment and unemployment change from month to month.

Unemployment Rate in Philippines decreased to 3.90 percent in May from 4.10 percent in April of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in Germany remained unchanged at 6.30 percent in July. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.