Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July 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 table shows the percentage of unemployed who were jobless for 27 weeks or longer in the United States in 2014, by state. In 2014, about 40 percent of the unemployed in Florida had been jobless for 27 weeks or longer.

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.

In the period from 2014 to 2024, the unemployment rate in Finland reached a peak of 9.6 percent in 2015 and, since then decreased rather steadily until 2020. In 2024, the unemployment rate was at 8.4 percent.

This data set contains the veterans unemployment rate in Maryland. Figures come from the Bureau of Labor Statistics, and are subject to revision.

In 2023, the unemployment rate in Finland was at 8.4 percent. Unemployment increased among the younger age groups during 2024, but the highest rate was measured among young people aged 15 to 24 years. The unemployment rate among 15 to 24 year-olds stood at 18.8 percent in 2024, while the rate for people aged 35 to 44 years was at 6.5 percent.

Youth Unemployment Rate in Nigeria decreased to 6.50 percent in the second quarter of 2024 from 8.40 percent in the first quarter of 2024. This dataset provides - Nigeria Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Youth Unemployment Rate in Tanzania decreased to 13.70 percent in 2014 from 14.90 percent in 2006. This dataset provides the latest reported value for - Tanzania Youth Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Department of Treasury and Finance (DTF) monitors economic conditions in the Victorian economy and prepares forecasts of the main economic indicators of those conditions twice yearly for the current and four-ensuing years (the out-years). The economic forecasts underpin the Government's fiscal outlook presented in the Budget and Budget Update. The key economic indicators forecast growth in real gross state product (GSP) and the level of nominal GSP; growth in employment and the unemployment rate; growth in wages; growth in consumer prices (the CPI) and population growth. For further information refer to the Macroeconomic indicators methodology for making forecasts of macro-economic indicators.

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, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

Brazil Unemployment Rate: Rondonia: Total data was reported at 8.400 % in Mar 2020. This records an increase from the previous number of 8.000 % for Dec 2019. Brazil Unemployment Rate: Rondonia: Total data is updated quarterly, averaging 7.500 % from Mar 2012 (Median) to Mar 2020, with 33 observations. The data reached an all-time high of 10.400 % in Mar 2018 and a record low of 3.600 % in Dec 2014. Brazil Unemployment Rate: Rondonia: Total data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GBA048: Continuous National Household Sample Survey: Unemployment Rate: by Sex.

Graph and download economic data for Unemployment Rate in Poughkeepsie-Newburgh-Middletown, NY (MSA) (POUG136UR) from Jan 2000 to Dec 2014 about Poughkeepsie, NY, unemployment, rate, and USA.

Graph and download economic data for Unemployment Rate - Bachelor's Degree and Higher, 25 Yrs. & over (LNU04027662) from Jan 1992 to Aug 2025 about 25 years +, tertiary schooling, education, household survey, unemployment, rate, and USA.

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

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 CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

The primary objective of LFS 2013-2014 was to provide current data on the employment and unemployment situation at national and governorate level using the preliminary version of the new standards concerning statistics of work, employment and labour underutilization on adopted by the 19th International Conference of Labour Statisticians (Geneva, October 2013).

---> The survey was then designed to meet five main measurement objectives as follows: 1- To provide current data on the number of employed, unemployed, and underemployed, and their demographic and social characteristics, including the size of women's participation in economic activity with a view to future policies in expanding their participation in the labour market. 2- To collect data on qualifications of the labour force and participation in training programmes of the youth population and other data requirements for improving the performance of employers through knowledge on the levels of skill available to them. 3- To measure the volume and characteristics of labour migration of Yemenis outside the country. 4- To provide information on the amount of wages and employment-related income in different occupations, branches of economic activity and sectors of employment. 5- To collect appropriate data for evaluating the microfinance projects funded through the Social Fund for Development.

Given the extent and diversity of data requirements, the survey was designed to spread over a one-year period, built around the five objectives of the survey. The core labour force survey was conducted throughout the four quarters of the survey period and incorporated the measurement of income from employment along the conventional items of data collection. Data on qualifications and participation in training was collected on the third quarter and on labour migration on the second quarter of the survey programme. Data collection on microfinance was undertaken as a separate survey over the four quarters.

Geographic coverage

Survey operations were carried out in all governorates except parts where recent events have disturbed the normal course of economic activity. In these circumstances, special procedures were used for compensation, either through the replacement of those areas with other areas having otherwise similar characteristics in the respective strata or through the adjustment of the sampling weights for missing values. There were 14 such cases, 5 each in quarters 1 and 4, and 2 each in quarters 2 and 3.

Analysis unit

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

Universe

The labour force survey covered the civilian non-institutional settled population excluding certain areas with difficult access or low population densities, in particular, the nomad population, displaced populations who are homeless, population living in public housing (boarding, hotels, prisons, hospitals, etc.), individuals enlisted in the Armed Forces, who are residing permanently within camps and do not spend most days of the year with their families. Similarly, for marine crews and expatriates outside the country and other categories of persons in remote islands.

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 CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

The sample design of the labour force survey of Yemen 2013-2014 is a two-stage stratified sample of enumeration areas in the first stage of sampling and a fixed number of sample households at the second stage of sampling. The resulting sample is spread evenly over the four quarters of the survey period.

Accordingly, the Central Statistics Organization (CSO) has drawn a stratified sample of census enumeration areas recomposed as primary sampling units (PSUs). Sample selection has been made with probability proportional to the number of households as determined in the 2004 population on census. In the second stage of sampling, after relisting of the sample enumeration areas, a fixed number of households (16 sample households) are drawn as clusters with equal probability from each sample enumeration area. The strata consist of the urban and rural areas of the 21 governorates in Yemen.

According to the sample design, urban areas are oversampled and rural areas under-sampled. This is because a relatively larger sample size is required in urban areas where heterogeneity is greater in comparison with rural areas. Also, because the cost of transportation and field operations is relatively greater in rural areas, it is more cost effective to under sample the rural areas relative to the less costly operations in urban areas. The differential sampling rates are then corrected through the sample weights so that the final results accurately reflect to the overall employment pattern.

The sample selection of the cluster of 16 households in each sample enumeration area was drawn after fresh listing of the totality of the households living in the sample enumeration area at the time of listing. This procedure updates the census information that dates back to 2004. The listing operations are carried out in each quarter before survey interviewing. The updated lists are send to CSO in Sana'a for data entry and sample selection of households for transmission to the survey team in each area. Instructions were given so that sample households that could not be found in the field or were absent or refused to be interview should not be substituted with other households as this procedure may introduce bias in the results. Instructions were also given that in cases where the minimum number of households in the sample enumeration areas was to be found to be less than the required 16 in each quarter, all households in the enumeration area should be taken in the sample.

The total sample size was determined on the basis of the requirement of producing national estimates of the unemployment rate with 1.5% margin of errors at the national level, assuming an overall non-response rate of 15%, and a design effect of 3. For the determination of the national sample size, the expected unemployment rate was set at 15% and the expected number of sample households to reach one person of working age, 15 years old and over, in the labour force was set at 0.6.

A more detailed description of the allocation of sample across governorates is provided in the report document available among external resources in English.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of the Yemen LFS 2013-2014 was designed on the basis of the ILO model LFS questionnaire (version A) and other national LFS questionnaires used in the region. The draft questionnaire was field tested with six households in Sana’a, each member of the field staff interviewing one sample household in his or her area. The experience gained in the field test was reviewed and led to some modifications of the draft questionnaire.

Apart from the cover page and the back page, the core LFS questionnaire contains 52 questions. There are 11 questions on the social and demographic characteristics of the household members in the household roster. In the individual questionnaire addressed to the working age population 15 years of age or older, there are 3 questions to identify the employed persons and 19 questions on their employment characteristics including timerelated underemployment followed by 8 additional questions on income from employment. The individual questionnaire also includes 5 questions to identify the unemployment and the potential labour force and 5 follow-up questions on unemployment characteristics.

Cleaning operations

----> Raw Data

Data processing involved data entry, coding, editing and tabulation of the survey results. Data entry was carried out in parallel with the interviewing of sample households. It was conducted at the Central Statistical Organization headquarter in Sana'a where all data processing operations except tabulation were centralized.

The supervisory staff of the data entry operations was responsible for editing the questionnaires before actual data entry. Editing at this stage involved review of the questionnaire regarding its filled-in contents including ensuring that there is no missing block of information for household members aged 15 years old and over and correct coding of occupation, branch of economic activity and other variables.

The data files were further processed at ILO headquarters in Geneva. They were first converted into a single file with 86,778 records and augmented with several fields, in particular, the sampling weights (“weight”) and the key derived variables: employed (E), unemployed (U), time-related underemployment (TRU), potential labour force (PLF) as well as other derived variables such as informal sector employment (IS) and informal employment (IE).

----> 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

Graph and download economic data for Unemployment Rate in Rochester-Dover, NH-ME (NECTA) (ROCH333UR) from Jan 2000 to Dec 2014 about Rochester, ME, NH, unemployment, rate, and USA.

Youth Unemployment Rate in Thailand increased to 5.30 percent in the first quarter of 2025 from 4.50 percent in the fourth quarter of 2024. This dataset provides - Thailand Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

According to the source, the unemployment rate in the Spanish autonomous community of Aragon reached **** percent in the third quarter of 2024, a slight increase compared to the previous quarter. There was an overall decrease in unemployment rates throughout the period under study.

Russia Unemployment Rate: North Western Federal District data was reported at 4.500 % in Dec 2017. This stayed constant from the previous number of 4.500 % for Nov 2017. Russia Unemployment Rate: North Western Federal District data is updated monthly, averaging 4.400 % from Apr 2014 (Median) to Dec 2017, with 45 observations. The data reached an all-time high of 5.100 % in Mar 2015 and a record low of 3.400 % in May 2014. Russia Unemployment Rate: North Western Federal District data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GB007: Unemployment Rate.