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 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 statistic shows the unemployment rate among people between 51 and 60 years in urban China in 2011, by gender. In 2011, ** percent of women between 51 and 55 years were registered as unemployed in China.

Syria SY: Unemployment Rate data was reported at 14.896 % in 2011. This records an increase from the previous number of 8.613 % for 2010. Syria SY: Unemployment Rate data is updated yearly, averaging 8.613 % from Dec 1989 (Median) to 2011, with 13 observations. The data reached an all-time high of 14.896 % in 2011 and a record low of 5.800 % in 1989. Syria SY: Unemployment Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.IMF.IFS: Labour Force, Employment and Unemployment: Annual.

Youth Unemployment Rate in Iran increased to 20.20 percent in the fourth quarter of 2024 from 19.40 percent in the third quarter of 2024. This dataset provides - Iran Youth Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The administrative unemployment rate reflects the proportion of people in the labour force who are unemployed, looking for a job and available for employment. It measures the imbalance between labour supply and demand. The 2011 Census data are the most recent data we currently have at the sub-communal level. The population of the Census is not strictly defined in the same way as that used for other labour market indicators. This can impact the results even if the orders of magnitude remain the same. Note: From 2011, the indicators are calculated on the basis of the Steunpunt WSE estimates, marked by two series breaks: in 2017, the method of estimating non-taxable students is changed and employees of international organisations were integrated into employed workers In 2019, the source that provides the number of outgoing border workers changes, leading to a decrease in employment, thus also in activity, and an increase in the unemployment rate that can be significant in some border municipalities.

Go to the IWEPS website for more information:

— the part “\2”

— the “\2”

— the IWEPS Working Paper n°13

Some thematic indicators are also available by statistical sectors on the website of “\2”

This statistic shows the unemployment rate in urban China in 2011, by age group. In 2011, *** percent of people between 21 and 25 years old were registered as unemployed in urban China.

This table contains unemployment rates (number of unemployed people aged 15 - 64 divided by those in the labour force in the area) by age group (15 - 24, 25 - 44, 45 - 64) calculated from the 2011 Census for the AURIN Social Indicators project.

Youth Unemployment Rate in Vietnam increased to 8.19 percent in the second quarter of 2025 from 7.93 percent in the first quarter of 2025. This dataset provides - Vietnam Youth Unemployment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The employment rate reports the number of people who actually have a job (working population) to the population aged 15 to 64, on an annual average.It gives an idea of the actual participation in employment of a population that could potentially work. The 2011 Census data are the most recent data we currently have at the sub-communal level. The population of the Census is not strictly defined in the same way as that used for other labour market indicators. This can impact the results even if the orders of magnitude remain the same. Note: From 2011, the indicators are calculated on the basis of the Steunpunt WSE estimates, marked by two series breaks: in 2017, the method of estimating non-taxable students is changed and employees of international organisations were integrated into employed workers In 2019, the source that provides the number of outgoing border workers changes, leading to a decrease in employment, thus also in activity, and an increase in the unemployment rate that can be significant in some border municipalities. Go to the IWEPS website for more information: — the part “\2” — the “\2” — the IWEPS Working Paper n°13 See also document “\2” Some thematic indicators are also available by statistical sectors on the website of “\2”

Definition: The unemployment rate reflects the percentage of unemployed persons aged 15 to under 65 in the labour force (employed and unemployed combined) of the corresponding age. For the definition of the unemployed according to the ILO concept, see Indicator 11.1. Note: On 1 January 2005, the survey concept was changed from a fixed reference week to a continuous survey throughout the year. From 2005, annual averages are reported. The figures for the years before 2005, on the other hand, refer to a fixed reference week in March, April or May. Note on the revision of the results for 2011 and 2012: The extrapolation framework for the microcensus has been changed: Up to the 2010 survey year, the extrapolation is based on the updated results of the 1987 census, and from 2011 on the updated results of the 2011 census. With the update for the 2013 survey year, the results for 2011 and 2012 were revised accordingly. Data source: IT.NRW, Microcensus

Nicaragua NI: Unemployment Rate: % Change over Previous Period data was reported at -19.879 % in 2011. This records a decrease from the previous number of -4.108 % for 2010. Nicaragua NI: Unemployment Rate: % Change over Previous Period data is updated yearly, averaging -0.585 % from Dec 1991 (Median) to 2011, with 18 observations. The data reached an all-time high of 51.316 % in 1991 and a record low of -19.879 % in 2011. Nicaragua NI: 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 Nicaragua – Table NI.IMF.IFS: Labour Force, Employment and Unemployment: Annual.

Unemployment Rate: Whole Country data was reported at 2.200 % in Sep 2018. This records an increase from the previous number of 2.190 % for Jun 2018. Unemployment Rate: Whole Country data is updated quarterly, averaging 2.210 % from Mar 2011 (Median) to Sep 2018, with 31 observations. The data reached an all-time high of 2.820 % in Mar 2011 and a record low of 1.800 % in Dec 2011. Unemployment Rate: Whole Country data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G034: Unemployment Rate: Quarterly.

Registered Unemployment Rate: Urban: Anhui: Hefei data was reported at 2.770 % in 2021. This records a decrease from the previous number of 3.060 % for 2020. Registered Unemployment Rate: Urban: Anhui: Hefei data is updated yearly, averaging 2.995 % from Dec 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 3.930 % in 2011 and a record low of 2.770 % in 2021. Registered Unemployment Rate: Urban: Anhui: Hefei data remains active status in CEIC and is reported by Hefei Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GF: Registered Unemployment Rate: Urban: Prefecture Level City.

Tunisia: Unemployment rate: The latest value from 2024 is 16.2 percent, an increase from 15.11 percent in 2023. In comparison, the world average is 6.80 percent, based on data from 176 countries. Historically, the average for Tunisia from 1991 to 2024 is 15.22 percent. The minimum value, 12.36 percent, was reached in 2007 while the maximum of 18.33 percent was recorded in 2011.

Nicaragua NI: Unemployment Rate: % Change data was reported at -29.051 % in Jun 2011. This records a decrease from the previous number of -27.236 % for Mar 2011. Nicaragua NI: Unemployment Rate: % Change data is updated quarterly, averaging -17.428 % from Jun 2010 (Median) to Jun 2011, with 5 observations. The data reached an all-time high of 8.361 % in Jun 2010 and a record low of -29.051 % in Jun 2011. Nicaragua NI: Unemployment Rate: % Change data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Nicaragua – Table NI.IMF.IFS: Labour Force, Employment and Unemployment: Quarterly.

Long Term Unemployment Rate in Montenegro increased to 16 percent in the fourth quarter of 2020 from 12.40 percent in the third quarter of 2020. This dataset provides - Montenegro Long Term 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, 2019-2023Please 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

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.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. The NSS 68th. round carried out during July'2011 - June'2012 was the nineth quinquennial round in the series covering subjects of (i) Household Consumer Expenditure and (ii) Employment and Unemployment.

Field work of the survey is carried out by the Field Operation Division ( FOD ) of National Sample Survey Office ( NSSO ) in which the central samples are covered. most of the State Governments also participate in the survey on matching sample size basis.

The National Sample Survey Office (NSSO) during the period July 2011 - June 2012 carried out an all-India household survey on the subject of employment and unemployment in India as a part of 68th round of its survey programme. In this survey, the nation-wide enquiry was conducted to generate estimates of various characteristics pertaining to employment and unemployment and labour force characteristics at the national and State levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (Schedule 10) adopting the established concepts, definitions and procedures. Based on the data collected during the entire period of survey, estimates of some key employment-unemployment characteristics in India and States have been presented in the NSSO published report on Key Indicators of Employment and Unemployment July'2011 - June'2012 ( 68th Round).

The main objective of the employment-unemployment surveys conducted by NSSO at periodic interval is to get estimates of level parameters of various employment and unemployment characteristics at national and State level. These statistical indicators on labour market are required for planning, policy and decision making at various levels, both within the government and outside. The critical issues in the context of labour force enquiries pertain to defining the labour force and measuring participation of labour force in different economic activities. The activity participation of the people is not only dynamic but also multidimensional: it varies with region, age, education, gender, level of living, industry and occupational category. These aspects of the labour force are captured in detail in the NSS survey on employment and unemployment and estimates are generated for labour force participation rate, worker population ratio, unemployment rate, wages of employees, etc. The indicators of the structural aspects of the workforce such as status in employment, industrial distribution and occupational distribution are also derived from the survey. Besides, from the data collected on the particulars of enterprises and conditions of employment, the aspects of employment in the informal sector and informal employment are reflected through the conceptual framework of the survey.

Geographic coverage

The survey covered the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remained inaccessible throughout the year.

Analysis unit

Households and Persons

Universe

Households and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

The 68th round (July 2011-June 2012) of NSS was earmarked for survey on 'Household Consumer Expenditure' and 'Employment and Unemployment'. The survey covered the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year. In addition to these, all the sample first stage units of the following areas were casualty in different sub-rounds: in sub-rounds 1, 2,3 and 4. In each of these four sub-rounds equal number of sample villages/ blocks (FSUs) was allotted for survey with a view to ensuring uniform spread of sample FSUs over the entire survey period. Attempt was made to survey each of the FSUs during the sub-round to which it is allotted. Because of the arduous field conditions, this restriction need not be strictly enforced in Andaman and Nicobar Islands, Lakshadweep and rural areas of Arunachal Pradesh and Nagaland.

Sample Design A stratified multi-stage design has been adopted for the 68th round survey. The first stage units (FSU) was the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) was households in both the sectors. In case of large FSUs, one intermediate stage of sampling was the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

Selection of the first-stage units: The various steps involved before making the selection of the FSUs are discussed at length in the following few paragraphs before taking up the issue of selection of USUs within FSUs.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (Panchayat wards in case of Kerala) constituted the sampling frame. For the urban sector, the list of latest available UFS blocks constituted the sampling frame.

Stratification of the first stage units: Within each district of a State/ UT, two basic strata were formed as follows: Within each sector of a State/ UT, the respective sample size will be allocated to the different strata/ sub-strata in proportion to the population as per census 2001. Allocations at stratum level were adjusted to multiples of 4 with a minimum sample size of 4. Allocation for each sub-stratum was 4. Equal number of samples were allocated among the four sub-rounds.

Selection of first-stage units: For the rural sector, from each stratum/ sub-stratum, required number of sample villages were selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001. For urban sector, from each stratum FSUs were selected by using Simple Random Sampling Without Replacement (SRSWOR). Both rural and urban samples were drawn in the form of two independent sub-samples.

Selection of Ultimate Stage Units (USU) within a FSU: The remaining paragraphs of this sub-section outlines the various steps leading to the actual selection of USUs within a FSU.

Criterion for hamlet-group/ sub-block formation: After identification of the boundaries of the FSU, it is to be determined whether listing was done in the whole sample FSU or not. In case the population of the selected FSU is found to be 1200 or more, it should be divided into a suitable number (say, D) of 'hamlet-groups' in the rural sector and 'sub-blocks' in the urban sector by more or less equalising the population as stated below.

approximate present population of the sample FSU no. of hg's/sb's to be formed

less than 1200 (no hamlet-groups/sub-blocks) 1
1200 to 1799 3
1800 to 2399 4
2400 to 2999 5
3000 to 3599 6
…………..and so on

For rural areas of Himachal Pradesh, Sikkim, Uttarakhand (except four districts Dehradun (P), Nainital (P), Hardwar and Udham Singh Nagar), Poonch, Rajouri, Udhampur, Doda, Leh (Ladakh), Kargil districts of Jammu and Kashmir and Idukki district of Kerala, the number of hamlet-groups were formed as follows:

approximate present population of the sample village no. of hg's to be formed

less than 600 (no hamlet-groups) 1 600 to 899 3 900 to 1199 4 1200 to 1499 5 .………..and so on

Formation and selection of hamlet-groups/ sub-blocks: In case hamlet-groups/ sub-blocks are to be formed in the sample FSU, the same should be done by more or less equalizing population.It was ensured that the hamlet-groups/ sub-blocks formed were clearly identifiable in terms of physical landmarks.

Two hamlet-groups (hg)/ sub-blocks (sb) were selected from a large FSU wherever hamlet-groups/ sub-blocks have been formed in the following manner - one hg/ sb with maximum percentage share of population always selected and termed as hg/ sb 1; one more hg/ sb selected from the remaining hg's/ sb's by simple random sampling (SRS) and termed as hg/ sb 2. Listing and selection of the households done independently in the two selected hamlet-groups/ sub-blocks. The FSUs without hg/ sb formation treated as sample hg/ sb number 1. It is to be noted that if more than one hg/ sb have same maximum percentage share of population, the one among them which is listed first in block 4.2 of schedule 0.0 treated as hg/ sb 1.

Listing of households: Having determined the hamlet-groups/ sub-blocks, i.e. area(s) to be considered for listing, the next step is to list all the households (including those found to be temporarily locked after ascertaining the temporariness of locking of households through