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.

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.

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.

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.

Number of job seekers in 2016 (Lorraine: 2014) Unemployment rate 2016 (in %, Lorraine: 2014) Territorial entities: Cantons (LOR, LUX), Kreise (RLP, SL), arrondissements (WAL) Statistical data sources: INSEE Grand Est, Statistisches Landesamt Rheinland-Pfalz, Statistisches Amt Saarland, STATEC, IWEPS. Calculations: LISER 2018 Geodata sources: ACT Luxembourg 2017, IGN France 2017, GeoBasis-DE / BKG 2017, NGI-Belgium 2017. Harmonization: SIG-GR / GIS-GR 2018

This statistic presents the unemployment rate for individuals aged 15 to 24 years in Bhutan from 2005 to 2016. In 2016, approximately *** percent of the labor force aged 15 to 24 years in Bhutan were unemployed.

United States US: Unemployment Rate: % Change over Previous Period data was reported at -10.616 % in 2017. This records a decrease from the previous number of -8.031 % for 2016. United States US: Unemployment Rate: % Change over Previous Period data is updated yearly, averaging -5.513 % from Dec 1949 (Median) to 2017, with 69 observations. The data reached an all-time high of 89.744 % in 1954 and a record low of -37.402 % in 1951. United States US: Unemployment Rate: % Change over Previous Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Labour Force, Employment and Unemployment: Annual.

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.

Number of job seekers 2000-2016: increase and decrease Change in unemployment rate 2000-2016 (in %). *Lorraine: 1999-2014, Saarland and Rhineland-Palatinate: 2001-2016 Territorial entities: Cantons (LOR, LUX), Kreise (RLP, SL), arrondissements (WAL) Statistical data sources: INSEE Grand Est, Statistisches Landesamt Rheinland-Pfalz, Statistisches Amt Saarland, STATEC, IWEPS. Calculations: LISER 2018 Geodata sources: ACT Luxembourg 2017, IGN France 2017, GeoBasis-DE / BKG 2017, NGI-Belgium 2017. Harmonization: SIG-GR / GIS-GR 2018

Unemployment rate of LWIA compared at state and national levels

Abstract

The Urban Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the inactive population of the country on continuous basis. The variables collected in the survey: socio-demographic characteristics of household members; economic activity during the last seven days and six months; including characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment; unemployment and characteristics of unemployed persons.

The general objective of the 2016 Urban Employment and Unemployment Survey is to provide statistical data on the distribution, characteristics and size of the economic activity status i.e. employed, unemployed population of the country at urban levels on annual basis. The specific objectives of the survey are to: • collect statistical data on the potential manpower and those who are available to take part in various socio-economic activities; • update the data and determine the size and distribution of the labour force participation and the status of economic activity for different sub-groups of the population at different levels of the country; and also to study the socio-economic and demographic characteristics of these groups; • identify the size, distribution and characteristics of employed population i.e. working in the formal or informal employment sector of the economy and earnings from paid employees by occupation and Industry...etc; • provide data on the size, characteristics and distribution of unemployed population and rate of unemployment; • provide data that can be used to assess the situation of women's employment or the participation of women in the labour force; and • generate annual time series data to trace changes over time

Geographic coverage

The survey covered all urban parts of the country except three zones of Afar and six zones of Somali, where the residents are pastoralists.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2007 Population and Housing Census was used as frame to select 30 households from the sample enumeration areas.

The country was divided into two broad categories. 1) Major urban centers: All regional capitals and five other major urban centers were included in this category. This category had a total of 16 reporting levels. A stratified two-stage cluster sample design was implemented to select the samples. The primary sampling units were EAs, from each EA 30 households were selected as a second stage unit.

2) Other urban centers: In this category, all other urban centers were included. 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 selected at the third stage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire that was used to collect the data had five sections:

Section - 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc.

Section - 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status.

Section - 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment.

Section - 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population.

Section - 5: Economic activities during the last six months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last six months, reason for not being active.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork, field supervisors and statisticians of the head and 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 was an important part of data processing operation to maintain the quality of the data. 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 matter experts from Manpower Statistics Team of the CSA.

Response rate

Response rate of the survey was 99.8%

Sampling error estimates

Estimation procedures, estimates, and CV's for selected tables are provided in the Annex II and III of the survey final report.

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.

Jamaica JM: Unemployment Rate data was reported at 13.200 % in 2016. This records a decrease from the previous number of 13.500 % for 2015. Jamaica JM: Unemployment Rate data is updated yearly, averaging 15.400 % from Dec 1985 (Median) to 2016, with 31 observations. The data reached an all-time high of 25.000 % in 1985 and a record low of 9.850 % in 2007. Jamaica JM: Unemployment Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Jamaica – Table JM.IMF.IFS: Labour Force, Employment and Unemployment: Annual.

As of July 2020, a total share of **** percent of Poles feared losing their employment. This was an increase of *** percent as compared to February of the same year.

United States US: Unemployment Rate: % Change data was reported at -10.616 % in 2017. This records a decrease from the previous number of -8.031 % for 2016. United States US: Unemployment Rate: % Change data is updated yearly, averaging -5.513 % from Dec 1949 (Median) to 2017, with 69 observations. The data reached an all-time high of 89.744 % in 1954 and a record low of -37.402 % in 1951. United States US: Unemployment Rate: % Change data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Labour Force, Employment and Unemployment: Annual.

Monaco MC: Unemployment: National Estimate: % of Total Labour Force data was reported at 6.330 % in 2016. This records an increase from the previous number of 3.600 % for 2000. Monaco MC: Unemployment: National Estimate: % of Total Labour Force data is updated yearly, averaging 4.965 % from Dec 2000 (Median) to 2016, with 2 observations. The data reached an all-time high of 6.330 % in 2016 and a record low of 3.600 % in 2000. Monaco MC: Unemployment: National Estimate: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Monaco – Table MC.World Bank.WDI: Employment and Unemployment. Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

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.

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.

Ghana GH: Unemployment: Modeled ILO Estimate: Youth: % of Total Labour Force Aged 15-24 data was reported at 4.901 % in 2017. This records an increase from the previous number of 4.718 % for 2016. Ghana GH: Unemployment: Modeled ILO Estimate: Youth: % of Total Labour Force Aged 15-24 data is updated yearly, averaging 9.824 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 19.947 % in 1999 and a record low of 4.511 % in 2015. Ghana GH: Unemployment: Modeled ILO Estimate: Youth: % of Total Labour Force Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank: Employment and Unemployment. Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

This dataset presents the unemployment rate of the population in small regions of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM). This indicator is the number of people unemployed, and the unemployment rate is the number of people unemployed divided by the number of people in the labour force. All indicators were extracted from the ABS Tablebuilder system using the usual residence profile. For usual residence data, the ABS moves people back to where they live, rather than using the location the data were collected (place of enumeration). Usual residence data is preferred for individual level data because it removes the effect of respondents travelling or holidaying. For more information please view the NATSEM Technical Report.