Approximately 14.2 percent of people aged 16 to 24 were unemployed in the United Kingdom in the first quarter of 2025, the highest of any age group in that month. During this time period, older age groups have had much lower unemployment rates than younger ones, who have consistently had the highest unemployment rate. For almost all the age groups, the peak in the unemployment rate was recorded in 2011 when almost a quarter of young working age people were unemployed. Young adults in the labor market In the provided time period, youth unemployment was at its lowest rate in the third quarter of 2022, when it was 10.3 percent. Since then, there has been a noticeable uptick in youth unemployment, which was 14.8 percent towards the end of 2024. A more long-term trend among this age group is the increase in economic inactivity, with 40.8 percent of 16 to 24-year-old's not in work or actively looking for work in 2024. Although students or people in training account for a high share of this economic inactivity, there has also been a rise in the proportion of young adults who are not in education, employment or training (NEET), which reached a ten-year-high of 13.2 percent in late 2024. Unemployment up from low baseline in late 2024 In 2022, the UK labor market, had very low levels of unemployment along with a record number of job vacancies. Throughout 2023 and 2024, this very tight labor market began to loosen, although is still quite low by historic standards. One indicator that has stood out since the COVID-19 pandemic, however, has been the number of people economically inactive due to being on long-term sick leave, which reached 2.82 million in the first quarter of 2024, and has been the main reason for economic inactivity in the UK since late 2021.

Unemployment: Worked Before: Age 40 to 44 data was reported at 88.000 Person th in Oct 2018. This records an increase from the previous number of 83.000 Person th for Sep 2018. Unemployment: Worked Before: Age 40 to 44 data is updated monthly, averaging 80.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 164.000 Person th in Jun 1999 and a record low of 59.000 Person th in Nov 2013. Unemployment: Worked Before: Age 40 to 44 data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.G020: 4 Weeks Job Search: Unemployment: Worked Before: By Age and Academic Qualification.

In 2024, around 6.2 percent of people aged 25 and older who had less than a high school diploma, were unemployed. After relatively high levels of unemployment across all education groups in 2020 due to the COVID-19 pandemic, unemployment levels have decreased in the subsequent years. The monthly unemployment rate in the U.S. can be accessed here and the unemployment rate for each U.S. state can be accessed here.

Unemployment: Worked Before: Age 60 and Over data was reported at 108.000 Person th in Oct 2018. This records a decrease from the previous number of 115.000 Person th for Sep 2018. Unemployment: Worked Before: Age 60 and Over data is updated monthly, averaging 44.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 320.000 Person th in Feb 2018 and a record low of 16.000 Person th in Nov 2002. Unemployment: Worked Before: Age 60 and Over data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G020: 4 Weeks Job Search: Unemployment: Worked Before: By Age and Academic Qualification.

Unemployment among teenagers (16 to 19 years) in the United States stood at 13.8 percent in October 2024. The unemployment rate for teenagers has typically been much higher than that of adults. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. The general unemployment rate by state can be found here, and the annual national unemployment rate can be found here. Youth unemployment Youth unemployment, unlike teen unemployment, includes unemployed individuals aged 16 to 24. It includes many more individuals who have either just finished school or are graduated and looking for work. An unemployed person is someone who is laid off, fired or quits their work and is still looking for a job. Even in healthy economies, unemployment occurs. There are many reasons behind the unemployment of young people, for example: an educational system mismatched between academic education and needs in labor markets. As of 2020, the agriculture, forestry and fishing sector had the highest global youth unemployment rate at 28.9 percent.

Unemployment rates of 25- to 29-year-olds, by educational attainment, Canada and jurisdictions. This table is included in Section E: Transitions and outcomes: Labour market outcomes of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

In the fourth quarter of 2024, the unemployment rate in South Africa was 27.2 percent among workers aged 35 to 44 years. The figure decreased from 27.7 percent in the same quarter of the previous year. This age group corresponded to the largest share of the labor force participation in the country. Among young South Africans (15 to 24 years), the unemployment rate was at its highest, at 59.6 percent.

Korea Unemployment: < 1Y Work: Age 40 to 44 data was reported at 67.000 Person th in Oct 2018. This records an increase from the previous number of 57.000 Person th for Sep 2018. Korea Unemployment: < 1Y Work: Age 40 to 44 data is updated monthly, averaging 66.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 133.000 Person th in Jul 1999 and a record low of 41.000 Person th in Nov 2016. Korea Unemployment: < 1Y Work: Age 40 to 44 data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G021: 4 Weeks Job Search: Unemployment: Worked Less Than 1 Year: By Age and Academic Qualification.

Unemployment Rate in Australia increased to 4.30 percent in June from 4.10 percent in May of 2025. This dataset provides - Australia Unemployment Rate at 5.8% in December - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Korea Unemployment: Worked Before: Age 15 to 19 data was reported at 6.000 Person th in Oct 2018. This records an increase from the previous number of 5.000 Person th for Sep 2018. Korea Unemployment: Worked Before: Age 15 to 19 data is updated monthly, averaging 18.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 67.000 Person th in Jul 1999 and a record low of 5.000 Person th in Sep 2018. Korea Unemployment: Worked Before: Age 15 to 19 data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.G020: 4 Weeks Job Search: Unemployment: Worked Before: By Age and Academic Qualification.

Unemployment Rate in South Africa increased to 32.90 percent in the first quarter of 2025 from 31.90 percent in the fourth quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

B0611 - 2002 Unemployed Population Aged 15 Years and Over (Excluding First Time Job Seekers). Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).2002 Unemployed Population Aged 15 Years and Over (Excluding First Time Job Seekers)...

Korea Unemployment: Worked Before: Age 55 to 59 data was reported at 79.000 Person th in Oct 2018. This records a decrease from the previous number of 87.000 Person th for Sep 2018. Korea Unemployment: Worked Before: Age 55 to 59 data is updated monthly, averaging 44.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 106.000 Person th in Aug 2018 and a record low of 18.000 Person th in Sep 2002. Korea Unemployment: Worked Before: Age 55 to 59 data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.G020: 4 Weeks Job Search: Unemployment: Worked Before: By Age and Academic Qualification.

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. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 .

The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them 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).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.) URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2) : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area) 7 " (other area) 8 another city with population 1 million and above

9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be collected, on the basis of which its means of livelihood will be identified as one of the following : "self-employed in non-agriculture " "rural labour" and "others" (see section Two for definition of these terms) . Also the area of land possessed as on date of survey will be ascertained from all households while listing. Now the households of sub-stratum 2 will be arranged in the order : (1)self-employed in non-agriculture, (2) rural labour, other households, with land possessed (acres) : (3) less than 1.00 (4) 1.00-2.49,(5)2.50-4.99, (6)

In 2024, the youth unemployment rate in Germany increased by 0.8 percentage points (+13.45 percent) compared to 2023. In total, the youth unemployment rate amounted to 6.7 percent in 2024. This increase was preceded by a declining youth unemployment rate.The youth unemployment rate refers to the share of the workforce aged 15 to 24 that is currently not working but is actively searching for work. It does not include the economically inactive population, such as the long-term unemployed or full-time students.Find more statistics on other topics about Germany with key insights such as labor participation rate among the total population aged between 15 and 64.

The Eurobarometer series is a unique cross-national and cross-temporal survey program conducted on behalf of the European Commission. These surveys regularly monitor public opinion in the European Union (EU) member countries and consist of standard modules and special topic modules. The standard modules address attitudes towards European unification, institutions and policies, measurements for general socio-political orientations, as well as respondent and household demographics. The special topic modules address such topics as agriculture, education, natural environment and resources, public health, public safety and crime, and science and technology. This round of Eurobarometer surveys includes the standard modules and covers the following special topics: (1) Social Climate, and (2) Science, Research and Innovation. Respondent's opinions were collected on life satisfaction, area of living, healthcare, pension system, unemployment benefits, cost of benefits, the way the country is run, cost of living and affordability of energy and housing, in present time, in next twelve months and compared to five years ago. Thoughts about why people live in poverty were collected, general trustworthiness of people, views on how to help solve social and economic problems and views about education. As it relates to Science Research and Innovation respondents were asked how people's actions will affect the following 15 years from now: fight against climate change, Protections of the environment, energy supply, health and medical care, job creation, availability and quality of food, as well as transport and transport infrastructure. Opinions were collected on priorities for science and technological innovation. Respondents were asked about their academic past in studying science and technology. Demographic and other background information collected includes age, gender, nationality, marital status and parental relations, occupation, age when stopped full-time education, household composition, ownership of durable goods, difficulties in paying bills, self-assessed level in society, self-assessed social class, and Internet use. In addition, country-specific data includes type and size of locality, region of residence, and language of interview (select countries).

This phenomenological study explored how recent college graduates navigated from school to work during a recent economic downturn. More specifically, the study endeavored to understand the lived experiences of recent college graduates in a period of transition. Schlossberg’s (1984) transition theory and Arnett’s (2000) emerging adult theory framed the study. The conceptual underpinnings of both theories provided a foundation to understand the role of higher educational attainment in graduates’ time spent in the labor market to secure employment in dismal employment conditions. The heuristic value of each of the theories advanced understanding of the factors shaping the experiences of recent college graduates. This study sought an in-depth understanding of how recent college graduates handled disappointments and the types of coping resources that helped them reconcile old and new identities, exchange old tasks for new tasks, move into new responsibilities while they continued to carry on responsibilities leading to graduation and challenges associated with seeking employment. Participants’ emic stories provided expansion of the school-to-work phenomenon typically relegated to statistical reporting in terms of quantifiable unemployment and underemployment rates. This study contributes to a body of work that highlights how recent emerging adult college graduates compete for jobs post-graduation within an anemic labor market. By expanding the breadth of specialized knowledge, the information captures what is essential to empower emerging adult college graduates entering a labor market influenced by record unemployment and suggests ways to further career development. This study documented the shift in the roles and responsibilities in the school-to-work phase, and the employment issues of a cadre of recent emerging adult college graduates who were influenced by the economic recession of 2007 and its impact on the present. Interview data from nine participants were transcribed and coded to determine emerging themes. Themes were aligned with the concepts within transition theory and emerging adult theory to increase understanding of issues facing recent college graduates in transition to employment. The analysis of participants’ lived experiences expanded the educational leadership knowledge base to impact praxis by mitigating the unexpected effects of economic downturn, as students move out of school and into the labor market. The phenomenon has expanded the knowledge base by identifying how to organize resources of academic or student affairs and career centers to maximize how to offer services relevant to today’s emerging adult college graduates. The study discovered that although emerging adulthood is defined as age-based, a new category isolating the breadth of understanding for emerging adults college graduates is crucial to document the impact of dismal employment trends. Economic downturn affects the ability for young adults to reconcile identities. Study participants reported a lack of knowledge about how to mitigate their student orientation to career socialization. This study exposed a need for integration of student development theory, historically germane to traditional-aged collegians, and career development theory to guide emerging adult college graduates toward their career goals. The disconnect between the mass production of degrees and skills essential to the labor market (Bivens, 2014 ), have an impact on emerging adults who are one of four generations competing for skilled jobs at competitive rates of pay.

Korea Unemployment: < 1Y Work: Age 60 and Over data was reported at 89.000 Person th in Oct 2018. This records a decrease from the previous number of 96.000 Person th for Sep 2018. Korea Unemployment: < 1Y Work: Age 60 and Over data is updated monthly, averaging 34.000 Person th from Jun 1999 (Median) to Oct 2018, with 233 observations. The data reached an all-time high of 278.000 Person th in Feb 2018 and a record low of 13.000 Person th in Feb 2003. Korea Unemployment: < 1Y Work: Age 60 and Over data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G021: 4 Weeks Job Search: Unemployment: Worked Less Than 1 Year: By Age and Academic Qualification.

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 66th. round carried out during July'2009 - June'2010 was the eighth 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 2009 - June 2010 carried out an all-India household survey on the subject of employment and unemployment in India as a part of 66th 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 number NSS KI (66/10) on Key Indicators of Employment and Unemployment July'2009 - June'2010 ( 66th 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. However, all the sample first stage units of both rural and urban areas of Leh, Kargil and Poonch districts of Jammu & Kashmir became casualty and therefore these districts were outside the survey coverage.

Analysis unit

Households and persons

Universe

Households and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

The 66th round (July 2009-June 2010) 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. All the sample first stage units of both rural and urban areas of Leh, Kargil and Poonch districts of Jammu & Kashmir became casualty and therefore these districts were outside the survey coverage. In addition to these, all the sample first stage units of the following areas were casualty in different sub-rounds: (i) in sub-rounds 1, 2, and 4, both rural and urban areas of Rajouri district of Jammu & Kashmir, (ii) in sub-round 2, urban areas of Lakhisarai district of Bihar, (iii) in sub-round 3, rural areas of Doda district of Jammu & Kashmir. The estimates of the different sub-rounds, therefore, excluded these areas. The period of survey was of one year duration starting on 1st July 2009 and ending on 30th June 2010. The survey period of this round was divided into four sub-rounds of three months' duration each, the 1st sub-round period ranging from July to September 2009, the 2nd sub-round period from October to December 2009 and so on. In each of these four sub-rounds equal number of sample villages/ blocks (FSUs) were allotted for survey with a view to ensuring uniform spread of sample FSUs over the entire survey period. Sample Design A stratified multi-stage design was adopted for the 66th round survey. The first stage units (FSU) were the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. In addition, two non-UFS towns of Leh and Kargil of Jammu & Kashmir were also treated as FSUs in the urban sector. The ultimate stage units (USU) were households in both the sectors. Hamlet-groups/sub-blocks constituted the intermediate stage whenever these were formed in the sample FSUs.

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. For non-UFS towns, frame consisted of the individual towns (only two towns, viz., Leh & Kargil constituted this frame).

Stratification of the first stage units: Within each district of a State/ UT, two basic strata were formed as follows: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district.

However, within the urban areas of a district, if there were one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district were considered as another basic stratum.

Sub-stratification: There was no sub-stratification in the urban sector. However, to net adequate number of child workers, for all rural strata, each stratum was divided into 2 sub-strata as follows:

sub-stratum 1: all villages with proportion of child workers (p) >2P (where P is the average proportion of child workers for the sate/ UT as per Census 2001)

sub-stratum 2: remaining villages

Allocation of FSU's among Strata: At the all-India level, a total number of 12784 FSUs were allocated for survey in the central sample. In addition, 24 State sample FSUs (16 for rural sector and 8 for urban sector) of Leh and Kargil districts of J & K were included in the central sample. The total number of sample FSUs was allocated to the States and UTs in proportion to population as per census 2001 subject to the availability of investigators and ensuring minimum sample allocation to each State/ UT. The State/ UT level sample size was allocated between two sectors in proportion to population as per census 2001 with double weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu, etc. did not exceed the rural sample size. A minimum of 16 FSUs (to the extent possible) was allocated to each state/ UT separately for rural and urban areas. Further the State level allocations for both rural and urban areas were adjusted marginally in a few cases to ensure that each stratum/ sub-stratum got a minimum allocation of 4 FSUs. Within each sector of a State/UT, the respective sample size was allocated to the different strata/ sub-strata in proportion to the population as per census 2001. Allocations at stratum/ sub-stratum level were adjusted to multiples of 4 with a minimum sample size of 4 and equal number of samples was 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.

Selection of hamlet-groups/sub-blocks: Selected FSUs with approximate population 1200 or more were divided into a suitable number (say, D) of 'hamlet-groups' in

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest Edition InformationFor the sixth edition (November 2019), a new version of the data file was deposited, with the 2018 person and well-being weighting variables included. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2015 2016 ACADEMIC ACHIEVEMENT ADULT EDUCATION AGE APPLICATION FOR EMP... APPOINTMENT TO JOB APPRENTICESHIP ATTITUDES BONUS PAYMENTS BUSINESSES CARDIOVASCULAR DISE... CARE OF DEPENDANTS CHILD BENEFITS CHILDREN CHRONIC ILLNESS COHABITATION COMMUTING CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY BENEFITS FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HOME BASED WORK HOME BUYING HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE ILL HEALTH INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEARNING DISABILITIES LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECREATIONAL EDUCATION RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SKIN DISEASES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS SPOUSES SQUATS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS VISION IMPAIRMENTS VOCATIONAL EDUCATIO... WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...