Young people not in education, employment or training (NEET), UK: Aug 2016

For April to June 2016:

There were 843,000 young people (aged 16 to 24) in the UK who were not in education, employment or training (NEET), a decrease of 22,000 from January to March 2016 and down 78,000 from a year earlier.

The percentage of all young people in the UK who were NEET was 11.7%, down 0.3 percentage points from January to March 2016 and down 0.9 percentage points from a year earlier.

Just under half (46%) of all young people in the UK who were NEET were looking for work and available for work and therefore classified as unemployed. The remainder were either not looking for work and/or not available for work and therefore classified as economically inactive.

This statistical bulletin contains estimates for young people not in education, employment or training (NEET) in the UK. An article providing background information is available on our website. The bulletin is published 4 times a year in February, May, August and November. All estimates discussed in this statistical bulletin are for the UK and are seasonally adjusted. The figures discussed in this statistical bulletin are obtained from the Labour Force Survey (a survey of households) and are therefore estimates, not precise figures.

This statistical bulletin is accompanied by a data table in Excel spreadsheet format.

Young people

For this release, young people are defined as those aged 16 to 24. Estimates are also produced for the age groups 16 to 17 and 18 to 24 and broken down by sex.

Education and training

A person is 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 (re)start
• 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 4 weeks

Employment

“In employment” includes all people in some form of paid work, including those working part-time. People not in employment are classed as either unemployed or economically inactive. Unemployed people are those who have been looking for work in the past 4 weeks and who are available to start work within the next 2 weeks. Economically inactive people are those who have not been looking for work and/or who are not available to start work. Examples of economically inactive people include those not looking for work because they are students and those who are looking after dependants at home. These definitions are based on those recommended by the International Labour Organisation (ILO).

NEET

Anybody who is not in any of the forms of education or training listed above and not in employment is considered to be NEET. Consequently, a person identified as NEET will always be either unemployed or economically inactive.

Relationship to other labour market statistics for young people

Our monthly labour market statistical bulletin includes the dataset table A06: “Educational status, economic activity and inactivity of young people”. The NEET statistics and the Table A06 statistics are both derived from the Labour Force Survey and use the same labour market statuses; however, the educational statuses are derived differently. For Table 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 Table 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.

There were 843,000 young people (aged 16 to 24) in the UK who were not in education, employment or training (NEET), a decrease of 22,000 from January to March 2016 and down 78,000 from a year earlier. For April to June 2016, 11.7% of all people aged 16 to 24 were NEET, down 0.3 percentage points from January to March 2016 and down 0.9 percentage points from a year earlier. Figure 1 shows the percentage of people aged 16 to 24 who were NEET over the last 5 years.

For April to June 2016, there were 62,000 people aged 16 to 17 who were NEET, up 5,000 from January to March 2016 and up 11,000 from a year earlier. There were 781,000 people aged 18 to 24 who were NEET, down 27,000 from January to March 2016 and down 89,000 from a year earlier.

Unemployment measures people without a job who have been actively seeking work within the last 4 weeks and are available to start work in the next 2 weeks. For April to June 2016, there were 390,000 unemployed young people (aged 16 to 24) who were not in education, employment or training (NEET), up 9,000 from January to March 2016 and down 41,000 from a year earlier. For April to June 2016 there were:

• 230,000 unemployed men aged 16 to 24 who were NEET
• 159,000 unemployed women aged 16 to 24 who were NEET

Economic inactivity measures people not in employment who have not been seeking work within the last 4 weeks and/or are unable to start work within the next 2 weeks. For April to June 2016, there were 453,000 economically inactive young people (aged 16 to 24) who were not in education, employment or training (NEET), down 31,000 from January to March 2016 and down 37,000 from a year earlier. For April to June 2016 there were:

• 179,000 economically inactive men aged 16 to 24 who were NEET
• 274,000 economically inactive women aged 16 to 24 who were NEET

The Labour Force Survey Quality and Methodology Information document contains important information on:

• the strengths and limitations of the data and how it compares with related data
• users and uses of the data
• how the output was created
• the quality of the output including the accuracy of the data

Further information about the LFS is available from:

• the LFS User Guide
• LFS Performance and Quality Monitoring Reports

Accuracy of the statistics: estimating and reporting uncertainty

The figures in this statistical bulletin come from the 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 like 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 that 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”.

For example, the total number of people not in education, employment or training (NEET) aged 16 to 24 for January to March 2016 was estimated to be 865,000. This figure had a stated 95% confidence interval of +/- 51,000. This means that we can be 95% certain that the true total number of people NEET aged 16 to 24 for January to March 2016 was between 814,000 and 916,000. However, the best estimate from the survey was that the total number of people NEET aged 16 to 24 was 865,000. The percentage of people NEET aged 16 to 24 for the same period was estimated at 12.0%, with a stated 95% confidence interval of +/- 0.7%. This means that we can be 95% sure that the percentage of people NEET was between 11.3% and 12.7%. Again, the best estimate from the survey was that the percentage of people NEET aged 16 to 24 was 12.0%.

Working with uncertain estimates

In general, changes in the numbers (and especially the rates) reported in this statistical bulletin between 3-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 +/- 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.