There were an estimated 765,000 young people (aged 16 to 24 years) in the UK who were not in education, employment or training (NEET) in April to June 2020; this was a decrease of 28,000 compared with April to June 2019 and was down by 6,000 compared with January to March 2020.
The percentage of all young people in the UK who were NEET in April to June 2020 was estimated at 11.1%; the proportion was down by 0.3 percentage points compared with April to June 2019 and down by 0.1 percentage points compared with January to March 2020.
Of all young people in the UK who were NEET in April to June 2020, an estimated 39.0% were looking for, and available for, work and therefore classified as unemployed; the remainder were either not looking for work and/or not available for work and were classified as economically inactive.
There were an estimated 765,000 young people (aged 16 to 24 years) in the UK who were not in education, employment or training (NEET) in April to June 2020. The number was down by 28,000 when compared with April to June 2019 and a decrease on the quarter of 6,000.
The total number of people aged 18 to 24 years who were NEET was 705,000.
Of the 765,000 people aged 16 to 24 years who were NEET in April to June 2020, 413,000 were men and 351,000 were women (a record low).
In April to June 2020, an estimated 11.1% of all people aged 16 to 24 years were NEET. The proportion was down by 0.3 percentage points from April to June 2019 and decreased on the quarter by 0.1 percentage points.
The percentage of those aged 18 to 24 years who were NEET was 13.0% in April to June 2020.
Figure 1 shows the percentage of people aged 16 to 24 years who were NEET over the last 10 years. The percentage had been gradually decreasing since the peak of 16.9% in July to September 2011 but has been relatively flat since the beginning of 2017, averaging 11.2%.
Nôl i'r tabl cynnwys
Unemployment measures people without a job who have been actively seeking work within the last four weeks and are available to start work in the next two weeks. In April to June 2020, there were an estimated 298,000 unemployed young people (aged 16 to 24 years) who were not in education, employment or training (NEET), down by 32,000 from April to June 2019 and down by 22,000 from January to March 2020.
In April to June 2020, there were an estimated 181,000 unemployed men aged 16 to 24 years who were NEET and 117,000 unemployed women aged 16 to 24 years who were NEET.Nôl i'r tabl cynnwys
Economic inactivity measures people not in employment who have not been seeking work within the last four weeks and/or are unable to start work within the next two weeks. In April to June 2020, there were an estimated 467,000 economically inactive young people (aged 16 to 24 years) who were not in education, employment or training (NEET), up by 4,000 from April to June 2019 and up by 16,000 from January to March 2020.
In April to June 2020, there were an estimated 232,000 economically inactive men aged 16 to 24 years who were NEET (a record high) and 234,000 economically inactive women aged 16 to 24 years who were NEET (a record low).Nôl i'r tabl cynnwys
Young people not in education, employment or training (NEET)
Dataset | Released 20 August 2020
Quarterly estimates for young people (aged 16 to 24 years) who are NEET in the UK.
Sampling variability for estimates of young people not in education, employment or training
Dataset | Released 20 August 2020
Labour Force Survey (LFS) sampling quarterly variability estimates for young people (aged 16 to 24 years) who are NEET in the UK.
For this release, young people are defined as those aged 16 to 24 years. Estimates are also produced for the age groups 16 to 17 years and 18 to 24 years and by sex.
Education and training
People are considered to be in education or training if any of the following apply:
- they are enrolled on an education course and are still attending or waiting for term to start or restart
- they are doing an apprenticeship
- they are on a government-supported employment or training programme
- they are working or studying towards a qualification
- they have had job-related training or education in the last four weeks
Young people not in education, employment or training (NEET)
Anybody who is not in any of the forms of education or training listed previously and not in employment is considered to be not in education, employment or training (NEET). Consequently, a person identified as NEET will always be either unemployed or economically inactive.
People not in the labour force (also known as economically inactive) are not in employment but do not meet the internationally accepted definition of unemployment because they have not been seeking work within the last four weeks and/or they are unable to start work in the next two weeks.
Employment measures the number of people in paid work or who had a job that they were temporarily away from (for example, because they were on holiday or off sick). This differs from the number of jobs because some people have more than one job.
Unemployment measures people without a job who have been actively seeking work within the last four weeks and are available to start work within the next two weeks.
A more detailed glossary is available.Nôl i'r tabl cynnwys
This statistical bulletin contains estimates for young people not in education, employment or training (NEET) in the UK. The bulletin is published quarterly in February or March, May, August and November. All estimates discussed in this statistical bulletin are for the UK and are seasonally adjusted.
Statistics in this bulletin are used to help monitor progress towards the Sustainable Development Goals (SDGs). Explore the UK data on our SDGs reporting platform.
Young people who are NEET (PDF, 89KB) provides background information. The article explains how missing information for identifying someone as NEET is appropriated based on individual characteristics.
In response to the developing coronavirus (COVID-19) pandemic, we are working to ensure that we continue to publish economic statistics. For more information, please see COVID-19 and the production of statistics.
We have reviewed all publications and data published as part of the labour market release in response to the coronavirus pandemic. This has led to the postponement of some publications and datasets to ensure that we can continue to publish our main labour market data. This will protect the delivery and quality of our remaining outputs and ensure we can respond to new demands as a direct result of the coronavirus.
For more information on how labour market data sources, among others, will be affected by the coronavirus pandemic, see the statement published on 27 March 2020. A further article, published on 6 May 2020, detailed some of the challenges that we have faced in producing estimates at this time.
Our latest data and analysis on the impact of the coronavirus on the UK economy and population is now available on our dedicated coronavirus web page. This is the hub for all special coronavirus-related publications, drawing on all available data.
Impact of the coronavirus on data collection
The Labour Force Survey (LFS) design is based on interviewing households over five consecutive quarters. Generally, the first of these interviews, called wave 1, takes place face-to-face, with most subsequent interviews, for waves 2 to 5, conducted by telephone.
During March, we stopped conducting face-to-face interviews, instead switching to using telephone interviewing exclusively for all waves. This initially caused a significant drop in response.
New measures have been introduced to improve this, which have increased sample sizes during April, May and June, although they are still below normal LFS sample sizes.
Impact of the coronavirus on survey imputation methodology
The normal imputation for non-response to the LFS relies on rolling forward previous responses. Although this method is adequate under normal circumstances, it is not designed to deal with the changes experienced in the labour market in recent months. A new experimental imputation methodology has been researched to improve the measurement of the labour market at this time.
Because of time and system constraints, it has not been possible to fully integrate this methodology into the results within this release, but early indications suggest that there is little impact from the use of existing methodology on the headline measures of employment, unemployment and economic inactivity (less than 0.1 percentage points).
Impact of the coronavirus on survey weighting methodology
Because of the impact on data collection, different weeks throughout the quarter have different achieved sample sizes. To mitigate this impact on estimates, the weighting methodology was enhanced to include weekly calibration to ensure that samples from each week had roughly equal representation within the overall three-month estimate. This meant that any impacts seen from changes in the labour market in those weeks would be fully represented within the estimates.
Impact of government measures to protect businesses on the Labour Force Survey estimates
During late March, the government announced a number of measures to protect UK businesses. This included the Coronavirus Job Retention Scheme (CJRS), also referred to as furloughing, and the Self-Employment Income Support Scheme (SEISS).
The Office for National Statistics (ONS) classifies people within the labour market in line with International Labour Organization (ILO) definitions. Under the ILO definition, employment includes employed persons “at work” (that is, who worked in a job for at least one hour) and employed persons "not in work" because of temporary absence from a job or working time arrangements.
Under the current schemes, it is likely that workers would expect to return to that job and would consider the absence from work as temporary. Therefore, those people absent from work under the current schemes would generally be classified as employed under ILO definitions.
Relationship to other labour market statistics for young people
Our monthly Labour market statistical bulletin includes the dataset A06: Educational status and labour market status for people aged from 16 to 24. The NEET statistics and the dataset A06 statistics are both derived from the LFS and use the same labour market statuses; however, the educational statuses are derived differently.
For dataset A06, the educational status is based on participation in full-time education only. For NEET statistics, the educational status is based on any form of education or training, as listed previously. Therefore, the dataset A06 category "not in full-time education" includes some people who are in part-time education and/or some form of training and who, consequently, should not be regarded as NEET.
More quality and methodology information on strengths, limitations, appropriate uses, and how the data were created is available in the LFS QMI.
After EU withdrawal
As the UK leaves the EU, it is important that our statistics continue to be of high quality and are internationally comparable. During the transition period, those UK statistics that align with EU practice and rules will continue to do so in the same way as before 31 January 2020.
After the transition period, we will continue to produce our labour market statistics in line with the UK Statistics Authority's Code of Practice for Statistics and in accordance with ILO definitions and agreed international statistical guidance.Nôl i'r tabl cynnwys
Accuracy of the statistics: estimating and reporting uncertainty
The figures in this statistical bulletin come from the Labour Force Survey (LFS), a survey of UK households. Surveys gather information from a sample rather than from the whole population. The sample is designed carefully to allow for this and to be as accurate as possible given practical limitations such as time and cost constraints, but results from sample surveys are always estimates, not precise figures. This means that they are subject to some uncertainty. This can have an impact on how changes in the estimates should be interpreted, especially for short-term comparisons.
We can calculate the level of uncertainty (also called “sampling variability”) around a survey estimate by exploring how that estimate would change if we were to draw many survey samples for the same time period instead of just one. This allows us to define a range around the estimate (known as a confidence interval) and to state how likely it is in practice that the real value the survey is trying to measure lies within that range. Confidence intervals are typically set up so that we can be 95% sure that the true value lies within the range – in which case, we refer to a “95% confidence interval”.
The total number of people not in education, employment or training (NEET) aged 16 to 24 years for April to June 2020 was estimated at 765,000. This figure had a stated 95% confidence interval of plus or minus 59,000. This means that we can be 95% confident that the true total number of people who were NEET and aged 16 to 24 years for April to June 2020 was between 706,000 and 824,000. However, the best estimate from the survey was that the total number of people who were NEET and aged 16 to 24 years was 765,000.
The percentage of people who were NEET and aged 16 to 24 years for the same period was estimated at 11.1%, with a stated 95% confidence interval of plus or minus 0.9 percentage points. This means that we can be 95% confident that the percentage of people who were NEET was between 10.2% and 12.0%. Again, the best estimate from the survey was that the percentage of people who were NEET and aged 16 to 24 years was 11.1%.
Working with uncertain estimates
In general, changes in the numbers (and especially the rates) reported in this statistical bulletin between three-month periods are small and are not usually greater than the level that is explainable by sampling variability. In practice, this means that small, short-term movements in reported rates (for example, within plus or minus 0.3 percentage points) should be treated as indicative and considered alongside medium- and long-term patterns in the series and corresponding movements in administrative sources, where available, to give a fuller picture.
Seasonal adjustment and uncertainty
Like many economic indicators, the labour market is affected by factors that tend to occur at around the same time every year; for example, school leavers entering the labour market in July and whether Easter falls in March or April. To compare movements other than annual changes in labour market statistics, such as since the previous quarter or since the previous month, the data are seasonally adjusted to remove the effects of seasonal factors and the arrangement of the calendar. Estimates discussed in this statistical bulletin are presented seasonally adjusted. While seasonal adjustment is essential to allow for robust comparisons through time, it is not possible to estimate uncertainty measures for the seasonally adjusted series.
Dataset table NEET 2 shows sampling variabilities for estimates of young people who are NEET derived from the LFS.Nôl i'r tabl cynnwys
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