1. Main facts and figures
- overall, 94% of pupils went into education, apprenticeships or employment for at least 2 terms after finishing key stage 4 in July 2016
- 5% of pupils had no sustained education, apprenticeship or employment, and the destination was unknown for a further 1%
- over 90% of pupils from nearly every ethnic group went into education, apprenticeships or employment – the exceptions were Gypsy/Roma and Traveller of Irish heritage pupils, where the figures were 66% and 73% respectively
- overall, the percentage of pupils going into education, apprenticeships or employment went up by 5 percentage points compared with 2010/11 (from 89% to 94%); a higher percentage of pupils did so from every ethnic group except Gypsy/Roma
- pupils from the Chinese and Indian ethnic groups were consistently the most likely out of all ethnic groups to go into education, apprenticeships or employment every year during the period studied
- Mixed White and Black Caribbean pupils were less likely to stay in education in 2016/17, compared with the national average
- in 2016/17, Gypsy/Roma and Traveller of Irish heritage pupils were the least likely to stay in education (at 56% and 55% respectively), but the most likely to go into employment (at 7% and 11% respectively); however, it is not possible to draw firm conclusions about these groups due to the small number of pupils in key stage 4
The ethnic categories used in this data
This data uses categories from the Department for Education’s school census, which is broadly based on the 2001 national Census, with 3 exceptions:
- Traveller of Irish Heritage and Gypsy/Roma pupils have been separated into 2 categories
- Sri Lankan has been added to the Asian/Asian British group but is not reported separately
- Chinese pupils have been assigned a separate category
These changes were made after consultations with local authorities and lobby groups.
The categories in the school census are as follows:
- Traveller of Irish heritage
- Any Other White background
Mixed/Multiple ethnic groups:
- White and Black Caribbean
- White and Black African
- White and Asian
- Any Other Mixed/Multiple ethnic background
- Sri Lankan
- Any Other Asian background
- Any Other Black/African/Caribbean background
Other ethnic group
Unknown (where no ethnicity is recorded)
Information about destinations is provided for both detailed and broad ethnic groups where possible and when the data is available.
The 6 broad categories used are as follows:
- Asian/Asian British
- Black/African/Caribbean/Black British
- Mixed/Multiple ethnic groups
- Other ethnic group
2. By ethnicity
|Ethnicity||Education, apprenticeships or employment||Education||Apprenticeships||Employment||No sustained education/employment||Unknown|
|Mixed White/Black African||93||89||3||2||5||1|
|Mixed White/Black Caribbean||91||83||4||3||8||1|
3. By ethnicity over time
|Mixed White/Black African||87||89||91||93||94||94||93|
|Mixed White/Black Caribbean||86||86||87||89||91||91||91|
Data from the national pupil database (NPD) is used to calculate education destinations. The NPD links pupil and student characteristics (for example, age, gender, and ethnicity) to school and college learning aims and attainment information for children in schools in England.
Five administrative data sources are used in compiling the NPD and have been used to determine pupils’ education destinations:
- individualised learner record (ILR) covering English further education providers and specialist post-16 institutions
- school census covering English schools (including pupil referral units)
- awarding body data
- alternative provision census
- Higher Education Statistics Authority (HESA) data covering UK universities
Since 2014/15, employment data and out-of-work benefit data has been linked to the national pupil database to form the longitudinal education outcomes (LEO) dataset. Along with local authority data, LEO data is used to calculate employment destinations.
Employment data came from HM Revenue and Customs (HMRC). Out-of-work benefit data came from the Department for Work and Pensions (DWP).
For all years, information on employment, training and NEET (not in education, employment or training) comes from local authority data from the National Client Caseload Information System (NCCIS).
The matching of these databases was undertaken at individual level using personal characteristics such as name, date of birth and postcode.
These statistics cover pupils who went to state funded mainstream schools.
Suppression rules and disclosure control
Suppression is applied to the destination data to ensure that individual students cannot be identified, as follows:
- any total with fewer than 11 students has had all of their data suppressed
- figures referring to outcomes for 1 or 2 individuals have been suppressed – in some cases, more figures have been suppressed if publishing them would affect the suppression of those figures referring to outcomes for 1 or 2 individuals
Any data with a sum of 0 is retained unless it reveals information about employment destinations.
These rules are also applied to percentages relating to small numbers, so that numerators of less than 3 are suppressed. Percentages are calculated using unrounded data.
To help preserve confidentiality, student numbers have been rounded to the nearest 5.
The Code of Practice for Official Statistics requires the Department for Education (DfE) to take reasonable steps to ensure that their published or disseminated statistics protect confidentiality.
For more information about DfE’s disclosure control procedures for its statistical releases please see DfE’s statistical policy statement on confidentiality.
All pupil numbers have been rounded to the nearest 5 in the data download. Percentages are calculated using unrounded data.
5. Data sources
Type of data
Type of statistic
Department for Education
Purpose of data source
The data is collected to help provide clear and comparable information on the success of schools and colleges in helping their students continue in education or employment.
6. Download the data
This file contains: Measure, Ethnicity, Ethnicity type, Time, Time type, Geography, Geography type, Geography code, Gender, Gender type, Destination, Value, Value type, Denominator, Numerator