Methodology

Graduate labour market outcomes (LEO)

• Published: 25 June 2026

Introduction

Background to the Longitudinal Educational Outcomes (LEO) dataset 

The Small Business, Employment and Enterprise Act 2015 enabled government, for the first time, to link higher education and tax data. This allows us to track graduates from higher education as they move into the workplace. For more information on the legal powers governing the dataset please see section 78 of the Small Business, Enterprise and Employment Act 2015 and sections 87-91 of the Education and Skills Act 2008. 

One of the advantages of linking data from existing administrative sources is that it provides a unique insight into the destinations of graduates without imposing any additional data collection burdens on universities, employers or members of the public. In addition, coverage is very high, which side steps many of the problems such as sample bias that are associated with  survey methodology, and also allows us to more robustly break down analyses by combinations of variables.

The LEO dataset links information about students, including;

• personal characteristics such as sex, ethnic group and age
• education, including schools, colleges and higher education institution attended, courses taken, and qualifications achieved
• employment and income
• benefits claimed

It is created by combining data from the following sources:

• the National Pupil Database (NPD), held by the Department for Education (DfE)
• Higher Education Statistics Agency (HESA) data on students at UK publicly funded higher education institutions and some alternative providers, held by DfE
• Individualised Learner Record data (ILR) on students at further education institutions, held by DfE
• employment data from the Real Time Information System (RTI). RTI contains information formerly collected on the P45 and P14 forms, held by His Majesty’s Revenue and Customs (HMRC)
• data from the Self-Assessment tax return, held by HMRC
• the National Benefit Database, Labour Market System and Juvos data, held by the Department for Work and Pensions (DWP)

By combining these sources, we can look at the progression of higher education leavers into the labour market.  

The privacy notice explaining how personal data in this project is shared and used can be found at Longitudinal education outcomes study: how we use and share data - GOV.UK (www.gov.uk) (opens in new tab) (opens in new tab). 

Data quality and coverage

Employment and earnings data 

The employment data covers those with employment and earnings records submitted through the Pay As You Earn (PAYE) system. These figures have been derived from administrative IT systems that, as with any large-scale recording system, are subject to possible errors with data entry and processing. While some data cleaning was necessary, the resulting data looks to provide a good reflection of an individual’s employment and earnings for the year. 

For the purposes of collecting taxes only the tax year of employment is needed, accurate start and end dates within the tax year are not required. For this reason, issues encountered with the employment data included records with duplicate dates and records with dates which were invalid for our intended use (for example, where an employment start date occurred after the end date). 

Additionally, a number of returns were found to have missing start dates due to, for example, the employer not forwarding a timely P45. The default dates recorded in the dataset are either 6 April (the first day of the tax year) or, where only an end date is known, the day before that end date. Similarly, for records where the employment is known to have come to an end within a tax year but the end date is not known, the record is given a default 5 April end date, the last day of the tax year.

Individuals can also have overlapping spells of employment. Before carrying out analysis, the records for each individual were cleaned and then merged into a single record to give a longitudinal picture of their employment and a total sum of their earnings in each tax year. Where uncertain dates appeared, other employments or benefits records for that individual were used to create a merged employment spell with a known start and end date.

Example 1: Two employment spells

Spell A                                          Start |---------| End 

Spell B             Unknown start |-----------------|--------------| End

Merged result                               Start |----------------------| End

In example 1, the start date of spell B is uncertain with its possible range shown in bold. In this instance we can merge the two records resulting in an employment spell with the start date of spell A and an end date from spell B. 

Any remaining uncertain dates were imputed through random sampling of gap lengths from a frequency distribution that was constructed from gaps with a known length. 

DWP/HMRC coverage 

Beginning in April 2013, the P45 reporting system was phased out in favour of the Real Time Information (RTI) system, which requires employers to submit information to HMRC each time an employee is paid. RTI offers substantial improvements to the P45 system in terms of data coverage, since employers must now provide information on all their employees if even one employee of the company is paid above the Lower Earnings Limit. The move to RTI means that data coverage is high for the 2015/16 to 2023/24 tax years used in this publication. 

We cannot currently distinguish between part-time and full-time work in the LEO data alone. This is further discussed in “Methodology - Annualised earnings” and “Methodology – Estimated FTE Salaries”  

As well as employment data for those who pay tax through PAYE, the employment data additionally includes those who pay tax through self-assessment. 

Self-assessment forms are completed by a range of people who for example are self-employed, have received income from investments, savings or shares and by people who have complicated tax affairs. A list of people who are required to complete a self-assessment return can be found at www.gov.uk/self-assessment-tax-returns/who-must-send-a-tax-return (opens in new tab) (opens in new tab). We have self-assessment earnings from 2013/14 onwards, which contains variables on: 

Earnings received through employment (PAYE)

Income from partnership enterprises

Income from sole-trader enterprises

Total earnings for the tax year from the self-assessment form.

We have used the income from partnership enterprises and income from sole-trader enterprises to ascertain graduates who are self-employed and their earnings from self-employment enterprises. We have taken a sum of these two variables, and where the sum of these is greater than £0, graduates are classified as self-employed. Where self-employment earnings are used, the earnings amount is the sum of these two variables. 

In the data received from DWP, an overseas flag is received to identify individuals who are known to be living overseas. Details on when an individual informs HMRC can be found at Tax if you leave the UK to live abroad - GOV.UK (www.gov.uk) (opens in new tab) (opens in new tab). For this analysis, individuals who are known to be overseas are excluded as their earnings and outcomes data is likely to be incomplete.  In order to identify these individuals as well as possible, we supplement the information from DWP with information provided by the Student Loans Company (SLC), who also record whether each graduate is living overseas. If either source (DWP or SLC) flag that an individual is living overseas, we assume that they are.

Graduate coverage 

In this publication’s data, we include first degree graduates and postgraduates attending Higher Education Providers (Higher Education Institutes (HEIs), Further Education Colleges (FECs) and Alternative Providers (APs)) in Great Britain.

For postgraduates, we only include those from HEIs Higher Education Institutes (HEIs) due to small numbers studying postgraduate degrees in Alternative Providers and Further Education Colleges. It should be noted that not many postgraduate courses are undertaken in these providers with the most common courses at level 7 (master’s degree) in PGCE / Education and Teaching, Business or Law. 

In the 2014/15 academic year, some other specialist providers in England were mandated to submit data to the Higher Education Statistics Agency (HESA). In the 2015/16 academic year, the coverage was extended to include all other specialist providers in England with undergraduate designated courses. For this reason, this publication only includes information for graduates from other specialist providers, one and three years after graduation. (Note that in line with HESA statistics, the University of Buckingham, a specialist provider, is reported with universities).

Young graduates (under 21 at the start of their course)

Some of the breakdowns in this release only cover young graduates (under 21 at the start of their course). This is due to low data coverage of graduates who were mature students (21 or over at the start of the course) or where including mature students would provide an unreliable comparison against trends within the young graduates group. For example, the free school meals (FSM) breakdown has been calculated using school records data, and for many of the mature graduates this data is not readily available due to them having left school before this information was collected. Another example, ‘Home region’ has been calculated on young graduates alone using information about where they lived prior to study. For mature graduates this information is not as likely to be their home region, because they are more likely to have geographically relocated between leaving school and starting their course. The breakdowns that only cover young graduates are prior attainment, FSM, IDACI, home region and residence. 

The estimates of FTE (full-time equivalent) salary also only cover young graduates. This is because the average weekly hours data we have used from the Labour Force Survey is for the 21-30 age group. Young graduates (under 21 at the start of their course) will fall into this age group at one, three and five years after graduation.

Degree level (postgraduates)

The level of qualification for postgraduates is grouped into three categories (level 7 taught, level 7 research and level 8). These are defined using the HESA "QUAL" variable (Student 2019/20 - Qualification awarded | HESA (opens in new tab) (opens in new tab)). 

Graduates are more broadly grouped into Level 7 and Level 8, more commonly known as Master’s degrees and doctoral degrees respectively. Enhanced undergraduate courses (e.g. MMath, MEng) that yield a postgraduate-level qualification are not included in our Level 7 population. These degree courses are included in our first degree population as you do not need to have completed a Level 6 qualification to apply for these courses. 

Level 7 data are further broken down into Level 7 (taught) for taught master’s degrees and Level 7 (research) for research masters degrees. 

Comparative outcomes data by post-18 pathway

In the comparative outcomes sections of the publication we compare outcomes during the 2023-24 tax year among those who completed their GCSE year (Year 11) ten full years earlier in 2012-13. We include in our base cohort all those whose attainment was recorded at the end of Year 11 in the NPD, which covers all schools in England. We match records from HMRC and DWP for these individuals ten years after they completed Year 11 during the 2023-24 tax year and derive information about their employment and earnings outcomes.

We compare individuals grouped by four post-18 pathways: those with qualifications up to level 3, those with a level 4 or level 5 qualification not including apprenticeships, those with a level 4 or level 5 apprenticeships, and those with a level 6 or higher qualification excluding apprenticeships. Individuals are assigned to each pathway based on whether they obtained a qualification at Level 4 or higher from any provider in the UK during the years after they completed their GCSEs up to the end of the 2021-22 academic year. If an individual obtained more than one qualification above Level 4, we group them based on the highest qualification obtained. We rank qualifications by Level, with an apprenticeship at Level 4 higher than a Level 4 non-apprenticeship, and similarly at level 5. We are not able to identify whether individuals obtained a higher qualification from a provider outside the UK, so some of these individuals may be included in a group with qualifications lower than those that they actually obtained. 

We exclude from this base the groups of individuals listed below: 

• Those who started courses during 2015-16 or 2017-18 (two and three years after completing GCSEs) with the aim of achieving a qualification at Level 6 or above but did not complete the course and were instead awarded a qualification at Level 4 or 5 or not awarded any qualification. This is because we expect the experiences of these individuals to be fundamentally different to those who started and completed Level 4 or 5 qualifications. 
• Individuals who obtained their highest qualifications from providers in Scotland, Northern Ireland and Wales.
• Individuals whose highest qualification was a Level 6 or above apprenticeship
• Individuals whom we know to have been living overseas during the 2023-24 tax year

After excluding these individuals, we have records for around 85% of the original cohort. Employment and earnings records are linked for 98% and 93% of these individuals, respectively.

Those who pursue different post-18 pathways have different characteristics, and these characteristics may affect employment and earnings outcomes, irrespective of the post-18 pathway chosen. We take this into account to a limited degree by presenting results separately for females and males, and by grouping individuals by their GCSE attainment. 

We also group those who obtained a qualification at level 4 or above during the first years after school (up to the academic year 2021-22) by the broad subject area they studied for their highest qualification. We do this because the type and availability of education and training pathways differ between these subject groups, which could generate potentially misleading results were we to present them aggregating all subjects together. Where an individual studied more than one subject for qualifications that count as their highest qualification, we assign their employment and earnings outcomes to each subject they studied by the proportion of time they spent studying that subject. We use the following three subject groupings which are often used in the wider research literature:

• STEM subjects (medicine, mathematics, chemistry, physics, biosciences, architecture, nursing, pharmacology, medical sciences, allied health, engineering, computing, geography & earth sciences, sport sciences, general sciences)
• LEM subjects (law, economics, business & management)
• Other subjects (English, languages, philosophy, creative arts, performing arts, sociology, politics, history, media, health & social care, education, general studies)

Individuals with qualifications up to level 3 are not grouped by subject. This is because most in this group had not specialised in a particular academic area.

Comparison between first degree and postgraduates

We present direct comparisons between the outcomes of first degree graduates and postgraduates, without splitting results by, for example, prior attainment. Some factors to consider when making comparisons between first degree graduates and postgraduates then are:

• Firstly, students who go on to postgraduate study are typically expected to have achieved a higher level of attainment in their first degree.
• Secondly, the distribution of graduates by subject studied is likely to be different for postgraduates compared to first degree graduates (more information on population distributions by subject studied can be found at What do HE students study? | HESA (opens in new tab) (opens in new tab)). If for example postgraduate degrees tend to be in higher earnings subjects then this would lead to the overall postgraduate average being higher. 

Higher Education provider coverage

In this publication, we include graduates from Higher Education Institutes (HEIs), Alternative Providers (APs) and Further Education Colleges (FECs).  HEIs are mainly universities and former APs are HE providers who did not receive recurrent funding from the Office for Students (OfS) or other public bodies and who are not further education colleges. Eligible students can access loans and grants from the Student Loans Company (SLC) on specific courses, referred to as designated courses.

NPD data coverage

For prior attainment at GCSE, income deprivation affecting children index (IDACI) and free school meals status (FSM), LEO data is linked to the National Pupil Database (NPD).  More information on the NPD can be found at Find and explore data in the National Pupil Database - GOV.UK (education.gov.uk) (opens in new tab) (opens in new tab).

For Prior Attainment, we link to the KS4 attainment datasets. The coverage of these are England only, so prior attainment data is not available for those who studied KS4 in Scotland, Wales or Northern Ireland. 

For IDACI and FSM, the school census data is linked to LEO data. The school census covers a variety of schools which are listed at Which schools and pupils to include - Complete the school census - Guidance - GOV.UK (www.gov.uk) (opens in new tab) (opens in new tab). Not every individual in the LEO data can be matched to a school census record, these are represented as “Not known” in the publication. This could be because a pupil does not attend a school that completes the school census (e.g. they are at a registered independent school), they went to school outside of England, we are unable to match their LEO record to an NPD record from the variables given, and other less frequent reasons.

Industry data coverage

The industry groups provided use the ONS Standard Industrial Classification (SIC) codes (opens in new tab) (opens in new tab) agreed in 2007 (SIC2007). SIC codes provide information on the type of economic activity the graduates’ employer is engaged in, not the occupation of the graduate. This has been linked to LEO using the employer enterprise reference from the IDBR.  

The IDBR covers over 2.5 million businesses in all sectors of the UK economy; however it does not include very small businesses. To be on the IDBR businesses must be registered either for VAT or PAYE. The Business Population Estimates publication provides figures for the number of UK businesses, including the small businesses excluded from the IDBR. The IDBR covers approximately 45% of the total UK business population. 

The IDBR data used in this dashboard is from datasets owned by the Office of National Statistics (ONS). The ONS does not accept responsibility for any inferences or conclusions derived from the IDBR data by third parties.  

Graduates who do not have a PAYE record (e.g. self-employed) cannot be linked and will therefore be classified as ‘unknown’. A graduate’s SIC code is the industry in which they earned the most in the tax year, and in the case where there were two industries in which the graduate earned an equal amount, we have classified these as ‘unknown’ since one cannot be chosen. The majority of the analysis uses the 21 industry sections, however in the dashboard, the industry by subject table is expandable to the 3-digit-code level (see this ONS interactive SIC hierarchy (opens in new tab) (opens in new tab)). 

Graduates whose only income comes from self-employment cannot be linked to a SIC code, and therefore will be classified as ‘Unknown’. 

Domicile Categories (UK, EU and overseas)

Domicile categories have been based upon graduates’ domicile prior to the start of their course, as recorded in the HESA student record for graduates from HEIs and APs and as recorded in the ILR for graduates from FECs. Graduates have been categorised into three top-level categories – UK, EU and Non-EU. Due to data quality issues with the domicile variable on the ILR in the 2003/04 and 2004/05 academic years, we have not included non-UK domiciled graduates from FECs in the tables for these years. 

UK domiciled refers to graduates domiciled in England, Scotland, Wales or Northern Ireland prior to the start of their course. We now include earnings and employment outcomes for graduates from Higher Education Institutions in Scotland and Wales as well as England, and include breakdowns for Scottish and Welsh domiciled graduates.

EU domiciled refers to graduates domiciled in the EU (with their country’s EU membership being determined at the start of their graduation year) other than in the . To expand further on this, over the period covered by this publication the membership of the EU has changed and hence different graduating cohorts consist of different sets of countries. Graduates have been classed as EU domiciled if their recorded country of domicile was a member of the EU at the start of their year of graduation. Table below details for which cohort(s) each country has been designated as part of the EU domiciled category. Countries listed include all of their European Union territories; for instance, Finland includes the territory of the Åland islands. 

Countries and territories included in the European Union category by graduating cohort 

Country/Territory	Graduating cohorts in which domicile is counted as EU domiciled
Austria  Belgium  Denmark  Finland  France  Germany  Gibraltar  Greece  Ireland   Italy  Luxembourg  Netherlands  Portugal  Spain  Sweden	All graduating cohorts  (2003/04, 2004/05, 2008/09, 2009/10, 2010/11, 2011/12, 2012/13, 2013/14, 2014/15, 2015/16, 2016/17. 2017/18, 2018/19, 2019/20, 2020/21, 2021/22)
Cyprus  Czech Republic  Estonia  Hungary  Latvia  Lithuania  Malta  Poland  Slovakia  Slovenia	2004/05, 2008/09, 2009/10, 2010/11, 2011/12, 2012/13, 2013/14, 2014/15, 2015/16, 2016/17, 2017/18, 2018/19, 2019/20, 2020/21, 2021/22
Bulgaria  Romania	2008/09, 2009/10, 2010/11, 2011/12, 2012/13, 2013/14, 2014/15, 2015/16, 2016/17, 2017/18, 2018/19, 2019/20, 2020/21, 2021/22, 2020/21, 2021/22
Croatia	2013/14, 2014/15, 2015/16, 2016/17, 2017/18, 2018/19, 2019/20, 2020/21, 2021/22

The Crown Dependencies of Jersey, Guernsey and the Isle of Man are counted as part of the UK.  Overseas domiciled refers to graduates domiciled in countries/territories not belonging to the European Union and not in the UK.

Note that country of domicile is not the same as nationality (as recorded on the HESA student record). For instance, in 2012/13, 91% of UK domiciled graduates were UK nationals, while 7% of EU domiciled graduates and about 4% of overseas domiciled graduates were UK nationals.

Methodology

Time period 

The earliest time period for which employment and earnings data is reported is one year after graduation. This refers to the first full tax year after graduation (YAG).  Hence, for the 2021/22 graduation cohort, the figures one year after graduation refer to employment and earnings outcomes in the 2023-24 tax year. This time period was used because the previous tax year overlaps with the graduation date, and so graduates are unlikely to have been engaged in economic activity for the whole tax year.  

Academic year 2021/22   |------------------|     

Tax year 2022-23                                   |------------------| 

Tax year 2023-24                                                           |------------------|   

In the ‘graduate outcomes’ sections of the latest publication, we look at one, three and five years after graduation, focussing on the 2023-24 tax year with some comparative analysis with 2017-18 to 2022-23 tax years. Thus, we look at employment and earnings outcomes in the 2023-24 tax year for graduates from the 2017/18, 2019/20 and 2021/22 academic years. For the 2017-18 tax year, graduates from the 2011/12, 2013/2014 and 2015/2016 academic years are considered, and the other tax years are similarly calculated using this method.

The table below shows this for all tax years and academic years. The cells represent years after graduation (YAG). Bold indicates it is a cohort available in this publication: 

		Tax Year
		2017-18	2018-19	2019-20	2020-21	2021-22	2022-23	2023-24
Academic year of graduation	2009/10	7 YAG	8 YAG	9 YAG	10 YAG	11 YAG	12 YAG	13 YAG
	2010/11	6 YAG	7 YAG	8 YAG	9 YAG	10 YAG	11 YAG	12 YAG
	2011/12	5 YAG	6 YAG	7 YAG	8 YAG	9 YAG	10 YAG	11 YAG
	2012/13	4 YAG	5 YAG	6 YAG	7 YAG	8 YAG	9 YAG	10 YAG
	2013/14	3 YAG	4 YAG	5 YAG	6 YAG	7 YAG	8 YAG	9 YAG
	2014/15	2 YAG	3 YAG	4 YAG	5 YAG	6 YAG	7 YAG	8 YAG
	2015/16	1 YAG	2 YAG	3 YAG	4 YAG	5YAG	6 YAG	7 YAG
	2016/17		1 YAG	2 YAG	3 YAG	4 YAG	5YAG	6 YAG
	2017/18			1 YAG	2 YAG	3 YAG	4 YAG	5YAG
	2018/19				1 YAG	2 YAG	3 YAG	4 YAG
	2019/20					1 YAG	2 YAG	3 YAG
	2020/21						1 YAG	2 YAG
	2021/22							1 YAG

Discussions with stakeholders about presenting comparative outcome statistics by post-18 pathway

1. Introduction

Until the 2026 release of this publication, comparative labour market outcomes for graduates and non-graduates were published in the ‘Graduate labour market statistics’ official statistics release (Release home - Graduate labour market statistics - Explore education statistics - GOV.UK). Due to limitations with the Labour Force Survey (LFS) and duplication of relevant statistics available from other sources, as well as user feedback, DfE decided to cease publication of the ‘Graduate labour market statistics’ release and to implement feedback to develop this new Graduate Labour Market Outcomes (LEO) release. This is a new annual release superseding both the ‘LEO Graduate and Postgraduate Outcomes’ publication and the ‘Graduate Labour Market Statistics’ publication, combining and expanding on the statistics published in those releases. 

In order for the new elements of this release to be as useful as possible for users, we held eight one-hour discussion meetings with participants from the following groups:

• Academics with expertise in Higher Education research
• Analysts from the Higher Education Statistics Authority
• Campaigners on Higher Education issues
• Journalists with expertise in Higher Education
• Officials from the Office for Students
• Officials within the Department for Education
• Representatives from the Higher Education Policy Institute
• Representatives from the Office for Statistics Regulation
• Representatives from Universities UK

The purpose of these conversations was to receive feedback about our ideas regarding how to produce and present new comparisons in employment and earnings outcomes between groups of graduates and non-graduates. We also wanted to invite stakeholders to suggest their own ideas about what the new release should look like and cover.

Each meeting was structured in the same way, as follows:

1. An introduction that explained the background and aims of the new comparative elements of the publication
2. Discussion of what should be included in the headline figures that are presented most prominently in the publication
3. Discussion of how we should present additional statistics available in the publication
4. Discussion of methodological challenges and mitigations

We conducted a textual data analysis technique generally referred to as content analysis to summarise the main points made during these discussion meetings. The rest of this section summarises (anonymously) the main points made by discussants, grouped by theme, and the decisions we made informed by those discussions.

These meetings were very informative and enlightening and we would like to thank everybody who contributed their time and expertise to improve the quality and usefulness of the publication.

2. Themes that apply to the whole publication.

General feedback we received from most stakeholders was that the intended audience / key users were most likely to be policy makers, regulators, and sector stakeholders (e.g. mission groups, representative bodies, and higher education providers), as well as academics. Although the statistics would be of relevance to prospective students, they would not be key users of the publication, as the same LEO data is presented in a form designed for prospective students on the Discover Uni website, managed by the Office for Students.

Stakeholders also emphasised that it is very important how we frame and present these statistics to ensure as much as possible that they are not misleading, misinterpreted or misunderstood. In particular, we should ensure that all statistics presented in the publication are carefully caveated such that it is clear what can and cannot be concluded from them.

3. Key questions and themes: presentation of headline statistics

3.1. Should we present one overall figure for the earnings or employment outcomes of each group - for example a single average figure of earnings for graduates compared against a single average figure for non-graduates?

Many of the stakeholders we spoke with felt that an overall headline figure would be useful because it is simple and easy to digest and key users will expect to see it. On the other hand, they were concerned that presenting a single statistic might easily be misleading. This is because the characteristics of graduates and non-graduates are quite different and these characteristics may affect outcomes regardless of the educational path chosen. Comparison of single overall figures for graduate and non-graduate earnings could be misinterpreted as a form of ‘graduate premium’ or the result of having a degree, whereas in fact the difference would partly reflect the different characteristics of graduates and non-graduates. Because of this, stakeholders were overall in favour of presenting a range as opposed to single overall earnings figures for graduates and non-graduates.

We decided to present ranges of outcomes for each group across key characteristics, as opposed to single headline statistics.

3.2. How should we present a range of values for each group? Which characteristic (or characteristics) should we take into account?

Almost all stakeholders agreed that it is important to take into account both sex and prior attainment when we compare employment and earnings outcomes between graduates and non-graduates, and so they argued that breakdowns by these characteristics should be included in the headline figures. However, they were aware that other characteristics such as socio-economic background are also important and would also explain much of the variation in outcomes. They warned against selecting just some of the salient characteristics for breakdowns, given that many drivers impact labour market outcomes in complex ways. However, they appreciated that there would inevitably be a trade-off between including breakdowns by all important characteristics and presenting headline information that does not confuse users.

The OSR advised that we should explain why we present headline figures broken down by particular characteristics. In relation to prior attainment, we should be very clear how this is defined and that it can be thought of partly as a proxy measure for earlier exposures and experiences, such as those related to levels of childhood deprivation and parental education.

We decided to present all figures broken down by sex, prior attainment and subject area, accompanied by clear explanations of why we have chosen these particular breakdowns. Although we hadn’t discussed breakdowns by subject area a great deal in our meetings, we included these breakdowns because we know from our previous LEO publications of outcomes among graduates and postgraduates and from other research, for example by the Institute for Fiscal Studies, that employment and earnings outcomes vary substantially between graduates who studied different subject areas. 

3.3. Should the headline statistics explicitly include the difference in earnings outcomes between graduates and non-graduates?

The discussions about presenting differences in outcomes between graduates and non-graduates were similar to those described in section 3.1. Some stakeholders felt that explicitly presenting differences in earnings outcomes would generate a higher risk of users misinterpreting those differences as the causal effect of getting a degree. However, stakeholders also noted that some users will calculate the difference if they wish to in any case, and presenting differences provides us the opportunity to do so in an appropriately caveated way.

Many stakeholders advised against using the term ‘graduate premium’ to describe differences in earnings outcomes given the simple comparisons we present in the publication. The use of this term to describe other simple comparisons of average graduate and non-graduate earnings has previously received criticism because it suggests a causal effect of getting a degree. For this reason, they suggested that we use more neutral and objective language.

We made the decision not to present any single statistics on earnings differences, but instead to present ranges of these differences together with contextual information.

3.4. Annualised earnings is a major limitation of the LEO data.

Stakeholders felt that the range of employment outcomes presented in the rest of the publication was useful, but that a major limitation with the LEO data is that it only includes information to derive annualised earnings. On the one hand, we cannot derive hourly earnings from HMRC data, and on the other, users tend to assume that annualised earnings means salary. This is particularly relevant to comparisons between graduates and non-graduates because these groups often have different working patterns.

Several stakeholders stressed that we make very clear what we mean by annualised earnings. In addition, they suggested that we could provide contextual information about the characteristics (for example, age and sex) of those most commonly in part time work. Stakeholders expressed the wish for more information about salaries and discussed using information from the Labour Force Survey (LFS) combined with LEO data to derive estimates of hourly wages. 

We decided to use contextual data from the LFS to provide illustrative estimates of full time equivalent salaries, but not in the headline figures given the limitations inherent in this methodological approach.

3.5. Where do people who achieve a Level 4 or Level 5 qualification fit in?

Stakeholders argued for including those individuals who had achieved a Level 4 or Level 5 qualification as a third group, and to compare outcomes between this group and graduates with a degree at Level 6 or above and those with qualifications up to Level 3. The reasons for this were that they comprise a substantial number of individuals, that numbers in this group will increase with the introduction of the Lifelong Learning Entitlement (LLE), and that this group has fundamentally different educational experiences from either those with an undergraduate degree or those with qualifications up to Level 3 only.

They highlighted that the introduction of the LLE will encourage adults to dip in and out of courses and questioned how this would affect which comparisons would be most useful in future.

We decided to include as a third comparison group those individuals with qualifications at Levels 4 and 5. Because we found large differences in outcomes between those with apprenticeships and other qualifications at this level, we also decided to separate out Level 4 and 5 apprenticeships as an additional comparison group.

4. Key questions and themes: presentation of additional statistics.

4.1. Presentation.

Stakeholders were content with additional breakdowns being presented in tables and charts together with the Table Builder tool. This is how information is presented in the rest of the Graduate and Postgraduate Outcomes publication.

They requested that we provide as much information as possible in the underlying data.

They also advised us to include as many breakdowns as we could, including by sex, disadvantage (measured by free school meals status, Income Deprivation Affecting Children Index IDACI, and Tracking Under Representation by Area TUNDRA), and details of special educational needs and disability (SEND). Stakeholders recommended additional breakdowns by graduate experience, including provider, class of degree, subject studied, and mode of study, the region in which individuals live when employment and earnings outcomes are measured, and the industry they work in. They also noted the importance of tracking cohorts of individuals during multiple years after they achieved qualifications to see how outcomes change over time, adjusted for inflation.

We have provided as many breakdowns and as much detail in the underlying data as it was possible to prepare accurately for this publication, and we hope to provide additional breakdowns going forward.

5. Key questions and themes: Methodological challenges and mitigations

5.1. Coverage.

Stakeholders advised us to explain very clearly which individuals and providers are covered in the publication, as well as the limitations of the data, such as the proportions of school records we have managed to link to data from other sources (HMRC, the Department for Work and Pensions, Higher Education Statistics Authority data, etc..)

Stakeholders expressed interest in outcomes for those who started but did not complete a degree at Level 6 or above and are awarded a Level 4 or Level 5 qualification but did not see this as a focus of the publication. Because they thought it likely that outcomes for this group might differ from those who had started a Level 4 or Level 5 course, they recommended that we exclude them from the analyses for our publication.

We decided to exclude from the analyses all individuals who started a course with the aim of achieving a qualification at Level 6 or above but graduated with a Level 4 or 5 qualification.

5.2. Comparison groups.

Stakeholders felt that comparison groups should be of a similar age and from the same school cohort. There was interest in comparing outcomes between those who had and had not taken a gap year before entering Higher Education, but we agreed that this was not a main focus for this part of the publication.

We decided to include in the analyses the cohort who completed KS4 in one particular academic year, and to restrict the group to those who had started the academic year aged between 14 and 16.

5.3. Which Key Stage should we use to measure prior attainment?

Stakeholders had different opinions about which Key Stage to use, with some advocating the use of Key Stage 5 (KS5) attainment or UCAS points because they represent the latest measure of attainment before entering university. Others argued that those with KS5 attainment are already a selective population and that KS5 courses are very diverse and difficult to compare. By contrast, some stakeholders advocated the use of KS1 or KS2 attainment data as a closer proxy to ‘true’ academic ability than KS4 or KS5.

Strong arguments for using KS4 attainment were that this is the last Key Stage that is compulsory and so coverage is higher, that what pupils study at KS4 is surprisingly uniform and hasn’t changed significantly over the years, that the subjects studied cover a broad range of skills, that university applications are made on the basis of KS4 results (as students apply for university before completing KS5), and that they are easy to explain and understand.

We decided to use attainment at the end of KS4 to measure prior attainment.

5.4. Grouping individuals by prior attainment.

Stakeholders emphasised the importance of including sufficient numbers within each prior attainment grouping within each post-18 pathway and sex. This is because it would be misleading to present outcomes that could be influenced by small numbers of ‘atypical’ individuals. For this reason, they recommended exploring the data to inform decisions about how individuals should be grouped by defined by prior attainment.

We have assigned individuals to five groups of equal size by prior attainment, based on exploration of cell sizes in the data. 

Employment outcomes 

We refer to a graduate as matched if they have been successfully matched to the Department for Work and Pensions’ Customer Information System (CIS) or if they have been matched to a further study instance on the HESA Student Record. Graduates who have not been matched to CIS or a further study record are referred to as unmatched. These graduates were not found on DWP’s Customer Information System (CIS), either because they had never been issued with a National Insurance number or because the personal details provided from the HESA data did not fulfil the matching criteria. These graduates are excluded from calculations performed for UK domiciled populations. This is as well as records that were matched and are known to be overseas. 

Matched graduates:

UK domiciled graduates who have been matched and are not known to be overseas are then placed in one of five outcomes categories. These are

1. Activity not captured

2. No sustained destination

3. Sustained employment, further study or both

Of which there are subset groups:

4. Sustained employment only

5. Sustained employment with or without further study

Unmatched graduates:

Unmatched graduates are included in the denominator when calculating employment outcomes for non-UK domiciled graduates and are placed in a separate ‘unmatched’ outcome category. For these populations the match rates are much lower and non-UK graduates are much more likely to leave the UK after graduation. Including these graduates in the calculations means we get a better indication of the proportion of graduates who have stayed in the UK to work or study after graduation, making it easier to compare countries with vastly different match rates.  

For non-UK domiciled graduates, the employment outcome categories should not be used as an indication of success in finding employment after graduation, it is likely that the majority of these graduates who are ‘unmatched’ or in ‘activity not captured’ are employed outside of the UK.  

If a graduate is unmatched on the CIS but has a further study record for the tax year in question, then they are counted as being in further study, and hence are not in the unmatched category. 

• Activity not captured 

Graduates in this category have been successfully matched to CIS but do not have any employment, out-of-work benefits or further study records in the tax year of interest. Reasons for appearing in this category include: moving out of the UK after graduation for either work or study, voluntarily leaving the labour force or death. 

• No sustained destination 

Graduates who have an employment or out-of-work benefits record in the tax year in question but were not classified as being in ‘sustained employment’ and do not have a further study record. 

• Sustained employment defined by PAYE data 

The ‘sustained employment’ measure aims to count the proportion of graduates in sustained employment in the UK following the completion of their course. The definition of sustained employment is consistent with the definition used for 16-19 accountability and the outcome-based success measures published for adult further education (see https://www.gov.uk/government/statistics/adult-further-education-outcome-based-success-measures (opens in new tab) (opens in new tab)). This definition looks at employment activity in the six-month October to March period of each tax year. A graduate needs to be in paid employment for at least one day in five out of six months between October and March of a given tax year to be classified as being in ‘sustained employment’ in the given tax year. If they are not employed in March, they must additionally have at least one day in employment in the April of the same calendar year to be counted as being in sustained employment.  

For instance, a graduate employed from 1st October 2021 to 5th January 2022 and then again from 30th March 2022 onwards would be classed as being in sustained employment in 2021/22 as although they are not employed in February 2022 they are employed in the other five months in the period from October 2021 to March 2022. However a graduate employed from 1st October 2021 to 28th February 2022 but not employed in March 2022, would not be considered as being in sustained employment unless they had a day in employment April 2022.  

Sustained employment defined by self-assessment data 

Self-assessment data captures the activity of individuals with income that is not taxed through PAYE, such as income from self-employment, savings and investments, property rental, and shares.  A full list of income sources that must be declared through a self-assessment return can be found here: https://www.gov.uk/self-assessment-tax-returns/who-must-send-a-tax-return (opens in new tab) (opens in new tab).  

For the purposes of this publication, individuals are classed as being in sustained employment in the tax year if they meet our definition of sustained employment based on PAYE or have returned a self-assessment form stating that they have received income from self-employment and their earnings from a Partnership or Sole-Trader enterprise are more than £0 (profit from self-employment). These individuals may or may not have an additional PAYE record. Individuals who have received income through self-assessed means other than self-employment, such as through rental of property, and do not have a PAYE record, are not classed as being in employment (either sustained or unsustained). Those who have made a loss from self-employment are currently excluded from sustained employment as we are unable to distinguish between those who made a loss and those who submitted self-assessment returns for other reasons. 

Further study 

A graduate is defined as being in further study if they have a valid higher education study record at any UK HEP on the HESA Student Record or on the alternative HESA Student Record that overlaps the relevant tax year. The further study does not have to be at postgraduate level to be counted. The purpose of this category is to identify how students spent their time in the relevant tax year and as such cannot be used to calculate the proportion of graduates who go on to postgraduate study. We have not counted instances lasting 14 days or less. Additionally, students enrolled on further education courses, on some initial teacher training enhancement, booster and extension courses, whose study status is dormant or who were on sabbatical are excluded from this indicator in line with our previous methodology.

Designated alternative providers were not required to return student level data to HESA prior to the 2014/15 academic year. In the 2014/15 academic year all alternative providers covered by HESA did submit student level data for the first time, and these are included in this publication where applicable. The University of Buckingham has historically returned HESA data every year and so is included in all cohorts. 

As a tax year overlaps with two academic years, some students would be coming to the end of their further study in the tax year in question and some would be starting their further study. For example, those who graduated in the 2015/16 academic year and went straight on to a one-year masters course would not be counted as being in further study in the 2017/18 tax year (one year after graduation) as their course would finish in July 2017. If a graduate from 2015/16 waited a year before starting their one-year masters course then they would typically be counted as being in further study in the 2017/18 tax year (one year after graduation) if their course started in September 2017 for instance. 

Sustained employment only 

Graduates are considered to be in sustained employment only if they have a record of sustained employment (as defined either via the PAYE or self assessment data) but no record of further study (as defined above).  

Sustained employment with or without further study 

Sustained employment with or without further study includes all graduates with a record of sustained employment (defined either via the PAYE or self assessment data), regardless of whether they also have a record of further study (as defined above). 

Sustained employment, further study or both 

Sustained employment, further study or both includes all graduates with a record of sustained employment or further study. This category includes all graduates in the ‘sustained employment with or without further study’ category as well as those with a further study record only. 

It is important to note that our definition of sustained employment does not distinguish between the different types of work that graduates are engaged in and so cannot provide an indication of the proportion of graduates who are employed in graduate occupations.

The below table summarises the type of activity people may have to be unmatched or to fall into one of the five outcomes categories. 

Table: Classification of graduate outcomes (Y indicates that the column is true for that outcome) 

LEO category	Further study	Sustained employment	Any employment	Out-of-work Benefits
Unmatched	-	Unmatched to CIS	Unmatched to CIS	Unmatched to CIS
Activity not captured	-	-	-	-
No sustained destination	-	-	Y	-
	-	-	-	Y
	-	-	Y	Y
Sustained employment only	-	Y	Y	-
	-	Y	Y	Y
Sustained employment, with or without further study	Y	Y	Y	-
	Y	Y	Y	Y
	-	Y	Y	-
	-	Y	Y	Y
Sustained employment, further study or both	Y	Unmatched to CIS	Unmatched to CIS	Unmatched to CIS
	Y	-	-	-
	Y	-	Y	-
	Y	-	-	Y
	Y	-	Y	Y
	Y	Y	Y	-
	Y	Y	Y	Y
	-	Y	Y	-
	-		Y	Y
Further study, with or without sustained employment	Y	Unmatched to CIS	Unmatched to CIS	Unmatched to CIS
	Y	-	-	-
	Y	-	Y	-
	Y	-	-	Y
	Y	-	Y	Y
	Y	Y	-	-
	Y	Y	Y	-
	Y	Y	-	Y
	Y	Y	Y	Y

Annualised earnings 

Earnings figures are only reported for those classified as being in sustained employment via PAYE and where we have a valid earnings record or where they are self-employed and have reported income of over £0 for that tax year. Those in further study are excluded, as their earnings would be more likely to relate to part-time jobs. 

Under our new methodology, PAYE and earnings from self-employment are treated differently when calculating annualised earnings. 

For each graduate who has been paid through the PAYE system, the earnings reported for them for a given tax year are divided by the number of days recorded in the employment spell in that same tax year. This provides an average daily wage, which is then multiplied by the number of days in the tax year to create their annualised earnings. 

This calculation has been used to maintain consistency with figures reported for further education learners after study (Further education: outcome-based success measures– Explore education statistics – GOV.UK (explore-education-statistics.service.gov.uk)). It provides students with an indication of the earnings they would receive when in stable and sustained employment for the whole tax year. 

For earnings from self-employment, raw earnings are used. Due to the nature of the Self-Assessment tax return, dates of self-employment are not required and therefore are not available to annualise the self-employment earnings in the same way that PAYE earnings are annualised. We are therefore assuming that earnings reported in the Self-Assessment tax return relate to a spell of (self-)employment covering at least the whole tax year.  

Where a graduate has income from both sustained employment paid through PAYE and though self-employment, the earnings used for this graduate is the sum of their annualised PAYE earnings and their raw earnings from self-employment. It should be noted that a graduate with a PAYE records (that does not reach the ‘sustained’ criteria) and a self-employment earnings record will be counted as being in ‘sustained employment’ but we do not include their earnings in the earnings calculation. This is to avoid the risk of annualising PAYE data that could be based on a very short earnings spell.  

The average annualised earnings calculated are slightly higher than the raw earnings reported in the tax year. This is because the earnings of those who did not work for the entire tax year will be higher when annualised. The difference between the annualised and raw figures decreases as time elapses after graduation. That is, the difference between median annualised earnings and median raw earnings is greater one year after graduation than five year after graduation. The trend follows for both graduates who are in PAYE employment only and graduates who earned income from both PAYE employment and self-employment.   

Information provided on the Self-Assessment tax return includes a field on earnings through PAYE employment, which we have used only where PAYE earnings are not present.  

It should be noted that LEO alone does not have data on the average number of hours worked per week. The annualised earnings presented may therefore show relatively low earnings for groups with high rates of part-time work. However, the next section explains how we use Labour Force survey data to provide auxiliary illustrative estimates of Full-time equivalent (FTE) salary in some results.  

Estimated FTE salaries

As information on part-time working is not available in the LEO data, in order to provide an alternative lens through which to compare wages across groups with different part-time working tendencies indicative estimates of FTE (full-time equivalent) salaries by sex, post-18 pathway (‘Level 6 and above’ graduates compared to ‘Up to Level 3’ non-graduates), and subject have been produced using data from the ONS Labour Force Survey (LFS). More information on the LFS can be found here: Labour Force Survey - Office for National Statistics (opens in a new tab) (opens in new tab).

From the LFS we have used weekly hours data for the 21-30 age range, broken down by sex, industry sector of main job (INDE07M), and graduate type (graduate or non-graduate). The data is aggregated over the eight annual quarters that make up the latest two financial years in the data. For example for adjusting earnings of graduates in the 2023-24 tax year we use LFS data that is aggregated over the time period 2022 Q2 to 2024 Q1.

To calculate illustrative FTE salary from annualised earnings we require estimates of weekly working hours for each group of interest, as well as of how many hours counts as full-time work.

For an estimate of full-time weekly hours, we use the average value over the same eight quarters from the following ONS Labour market statistics time series (LMS) data: Average actual weekly hours of work for full-time workers - Office for National Statistics (opens in a new tab) (opens in new tab). For adjusting earnings in the 2023-24 tax year, the average full-time weekly hours value used was 36.4 hours.

To calculate average weekly hours by sex and graduate type, the LFS total weekly hours and weighted population numbers have been summed across all industry sectors, to get total weekly hours and total weighted population for male and female graduates and non-graduates. The total weekly hours are divided by the weighted population to calculate the average weekly hours by sex and graduate type.

To calculate average weekly hours by sex and subject, from the LEO data we know the proportion of male and female graduates in each subject who go on to work in each industry sector and from the LFS data we have the average weekly hours by industry sector and sex. Therefore, we can calculate an indicative average weekly hours value for each subject as the sum of each industry’s weekly hours weighted by the share of that subject entering that industry. For example, if 20% of male graduates in subject X go in to industry A which has an average of 40 weekly hours, 50% in to industry B which has average of 37 hours, and 30% in to industry C which has an average of 32 hours, then the average weekly hours for subject X is (0.2 * 40) + (0.5 * 37) + (0.3 * 32), which is equal to 36.1.

Using the average weekly hours figures calculated from the LFS data, and the average full-time weekly hours value of 36.4, we calculate FTE factors for each sex and graduate typeand subject grouping, where the FTE factor is the average weekly hours divided by the average full-time weekly hours. The FTE factor is then applied to the median annualised earnings of each group, to produce an illustrative estimated median FTE salary. For total sex (female and male combined) FTE salary estimates, these are calculated as a population-weighted average salary.

Given the limitations inherent in this methodology relying on survey data, these FTE salary estimates should be treated as only auxiliary figures, providing illustrative estimates of how certain groups may have more or less difference between their annualised earnings and FTE salary.

It is worth noting two further key things when interpreting the by subject median FTE salary estimates and comparing to median annualised earnings which do not account for part-time working:

1. The female/male graduate population split within a subject affects the extent to which the median earnings are adjusted to account for part-time working. Since women tend to have higher levels of part-time working than men, the median earnings of subjects with more female graduates will have a greater uplift. For example, Nursing and midwifery is a subject with a high proportion of female graduates, and therefore has one of the biggest differences between unadjusted median annualised earnings and estimated FTE salaries.
2. The other aspect affecting the FTE salary estimates by subject is the variation in average weekly hours by industry sector. The LFS data uses 9 industry sector categories, which groups the 21 industry sections used elsewhere in this publication (e.g. as used in the LEO industry dashboard). The groupings are set out in table X below. Subjects such as Medicine and dentistry have a relatively large difference between unadjusted median earnings and estimated FTE salaries because a high proportion of medicine and dentistry graduates go on to work in the ‘O,P,Q - Public admin, education and health’, which is an industry sector with fairly low average weekly hours – even though medicine and dentistry graduates specifically may actually work relatively high weekly hours. This highlights a key limitation of the methodology.

INDE07M industry sector of main job groups	SIC2007 industry sections
A - Agriculture, forestry and fishing	A: Agriculture, forestry and fishing
B,D,E - Energy and water	B: Mining and quarrying D: Electricity, gas, steam and air conditioning supply E: Water supply; sewerage, waste management and remediation activities
C -Manufacturing	C: Manufacturing
F - Construction	F: Construction
G,I -Distribution, hotels and restaurants	G: Wholesale and retail trade; repair of motor vehicles and motorcycles I: Accommodation and food service activities
H,J -Transport and communication	H: Transportation and storage J: Information and communication
K,L,M,N - Banking and finance	K: Financial and insurance activities L: Real estate activities M: Professional, scientific and technical activities N: Administrative and support services activities
O,P,Q - Public admin, education and health	O: Public administration and defence; compulsory social security P: Education Q: Human health and social work activities
R,S,T,U - Other services	R: Arts, entertainment and recreation S: Other service activities T: Activities of households as employers; undifferentiated goods- and services- producing activities of households for own use U: Activities of extraterritorial organisations and bodies

Calculating quintiles (for IDACI and Prior Attainment)

IDACI and prior attainment are grouped into quintiles when providing breakdowns of graduate outcomes. For these variables, quintiles are calculated within each academic year by ranking pupils by their IDACI (or prior attainment) scores and dividing into five equal sized groups.

Where pupils have records in multiple academic years, the most recent record is retained.

Job quality measure

The job quality measure we use is derived by linking the Higher Education Statistics Authority (HESA)'s Graduate Outcome Survey (GOS) data to the Department for Education's LEO dataset and scaling the GOS results to the full LEO graduate population. The GOS results are scaled to the LEO graduate population using a method called multiple imputation.

Data sources are:

• The Graduate Outcomes Survey (GOS), which all graduates are invited to complete 15 months after graduation. This includes questions that measure some key aspects of job quality. Each year, around 50% of graduates complete the GOS. More information on HESA's job quality measure design is available here: The value of a non-financial job quality measure in exploring graduate outcomes - Summary | HESA (opens in new tab) (opens in new tab). 
• The LEO dataset, which includes additional outcomes, such as earnings. The coverage of the LEO dataset is much closer to 100%, as it uses administrative data. 

The job quality measure we consider covers first degree graduates aged 17 to 20 at the start of their course who, during the relevant tax year, were in sustained employment in England, Scotland or Wales.

This measure is generated by averaging the scores from three Graduate Outcome Survey questions, each rated on a scale from 1 to 5: 

• To what extent do you agree or disagree with the statement: My current work is meaningful
• To what extent do you agree or disagree with the statement: My current work fits with my future plans
• To what extent do you agree or disagree with the statement: I am utilising what I learned during my studies in my current work

For respondents with missing values in for one or two of the three questions, the job quality score is taken as the score for the one question that was answered or the average of the two scores if two questions were answered. For respondents with missing values in all of these questions, we impute the total job quality scores using the data from respondents with complete responses.

The statistical technique of multiple imputation is used to impute values of job quality for graduates who did not participate in the GOS survey. This is important to do for two reasons:

• First, by increasing the number of graduate job quality scores through imputation, any patterns that emerge in the results are more likely to be statistically significant. 
• Second, it is possible that graduates who responded to the GOS were not a representative sample of all graduates in relation to job quality. For example, it may be that those who responded were, on the whole, happier in their jobs than those who did not. Multiple imputation adjusts for this response bias.

Graduate characteristics such as sex, age, FSM status, POLAR4, home region, ethnicity, prior attainment, mode of study, subject studied (for those with dual honours, we classify their subject as the one they spent the most studying), whether multiple subjects are studied, institution type, and provider category (e.g. Russell Group, top third, or neither) were used in the multiple imputation method to impute missing job quality scores.

Rounding and suppression rules 

We apply rounding and suppression rules to help minimise the risk of someone being identifiable from our data (also known as Statistical Disclosure Control), and to remove outcomes based on small populations which could potentially be misleading.  All calculations done for this publication are executed on the unrounded figures with the only the final result presented as rounded. 

The following rounding rules have been applied to this publication:

• All monetary values have been rounded to the nearest £100
• All population counts have been rounded to the nearest 5.
• All percentages have been rounded to 1 decimal place.
• All percentage values presented in the publication text are calculated from unrounded values rather than rounded values presented in the tables. 

The following suppression rules have been applied to this publication:

• Earnings and employment outcomes based on fewer than 12.5 FPE (i.e. rounding to 10 or less) have been suppressed.

Definitions

Sex

For graduates from universities, higher education colleges and other specialist providers of higher education, this field is collected by HESA. We filter our data to only include individuals who are recorded as ‘Male’ or ‘ Female’ to avoid the risk of disclosure for individuals who are recorded as ‘Other’. 

For graduates from FECs, the field is collected in the ILR. For these individuals, ‘Male’ and ‘ Female’ are the only possible entries in the field. 

Ethnicity

The ethnicity breakdowns provided use groupings in line with HESA and ONS published data. Detailed ethnicity breakdowns are provided in the publication and breakdowns of broader ethnic groups can be viewed in the downloadable main tables document.

Age

Age breakdowns use the age at the start of the course. This is calculated as their age on the 30th September of the academic year e.g. for individuals starting in the 2012/13 academic year, their age on the 30th September 2012.

Some of the breakdowns in this release only cover young graduates (under 21 at the start of their course). Details on the reason for this can be found under ‘Data quality and coverage’.

Subject areas 

HESA maintain the Common Aggregation Hierarchy (CAH) to categorise subjects and to maintain consistency across years using different categorisation systems. We typically use level 2 of the CAH to report breakdowns by subject area.  

The 35 CAH level 2 subject categories are listed below.

CAH Code	Subject
CAH01-01	Medicine and dentistry
CAH02-02	Pharmacology, toxicology and pharmacy
CAH02-04	Nursing and midwifery
CAH02-05	Medical sciences
CAH02-06	Allied health
CAH03-01	Biosciences
CAH03-02	Sport and exercise sciences
CAH04-01	Psychology
CAH05-01	Veterinary sciences
CAH06-01	Agriculture, food and related studies
CAH07-01	Physics and astronomy
CAH07-02	Chemistry
CAH07-04	General, applied and forensic sciences
CAH09-01	Mathematical sciences
CAH10-01	Engineering
CAH10-03	Materials and technology
CAH11-01	Computing
CAH13-01	Architecture, building and planning
CAH15-01	Sociology, social policy and anthropology
CAH15-02	Economics
CAH15-03	Politics
CAH15-04	Health and social care
CAH16-01	Law
CAH17-01	Business and management
CAH19-01	English studies
CAH19-02	Celtic studies
CAH19-04	Languages and area studies
CAH20-01	History and archaeology
CAH20-02	Philosophy and religious studies
CAH22-01	Education and teaching
CAH23-01	Combined and general studies
CAH24-01	Media, journalism and communications
CAH25-01	Creative arts and design
CAH25-02	Performing arts
CAH26-01	Geography, earth and environmental studies

It is important to note that each CAH subject area can sometimes include a diverse range of subjects, some of which will lead to significantly different employment and earnings outcomes. 

We have also provided graduate outcomes by CAH3 subject  in the underlying data files available for download.

Income Deprivation Affecting Children Index (IDACI)

The IDACI measures the proportion of all children aged 0 to 15 living in income deprived families. Family is used here to indicate a ‘benefit unit’, that is the claimant, any partner and any dependent children for whom Child Benefit is received. 

Free School Meals (FSM)

For FSM, we use the FSM6 variable as used in the Pupil Premium calculations. This looks at school census records for the individuals to see if they have ever been eligible for FSM in the last six years from the date of the census.  

We use data from the Spring census when the individual was in Year 11. The Spring census is used in finalising Pupil Premium funding meaning it is more likely to be accurate. We use the Year 11 census as it has a better coverage than Sixth Form and FE colleges do not have to return the school census.  

FSM6 is used as it ensures we pick up all individuals who have seen some disadvantage during secondary school (e.g., someone could be eligible for FSM for Year 6 to Year 10 but not Year 11, using FSM6 picks this up whereas FSM would not).  

In this publication, an individual is counted in the “FSM” category depending on their most recent FSM6 record census value in KS4. 

POLAR (Participation Of Local Area) 

The Participation of Local Areas (POLAR) classification places local areas into five groups, based on the proportion of 18 year olds who enter higher education at age 18 or 19. POLAR4 is the iteration used in this publication. Detailed information about the POLAR methodology is available from the OfS at www.officeforstudents.org.uk/data-and-analysis/polar-participation-of-local-areas/ (opens in new tab). 

Here, we publish the proportion of non-mature matched graduates whose postcode on the student record placed them in quintile 1 (the most disadvantaged group) of POLAR4 before applying for or entering higher education. This information is split by subject studied, institution, gender and year after graduation.  

For mature students, their postcode immediately before entering higher education is less likely to be indicative of the environment they grew up in, and hence their POLAR classification would have to be interpreted differently from that of non-mature students. We therefore exclude mature students from our POLAR measure. 

HESA do not publish POLAR figures for Scotland, as Scotland’s relatively high participation rate and the high proportion of higher education students in further education colleges could misrepresent Scottish contributions to widening participation. Following that line of reasoning, this publication does not include POLAR figures for Scottish HEIs either. 

Prior Attainment

 We think of prior attainment partly as a marker of a wide range of background characteristics, including parental education and expectations, level of deprivation, and educational opportunities, as well as cognitive preferences. Individuals are grouped into five categories referred to as quintiles with roughly equal numbers in each category, based on attainment in their top 8 GCSEs. Below are the average equivalent grades pupils achieved within each quintile:

• Quintile 1: 4Cs and one E
• Quintile 2: 8Cs
• Quintile 3: 4Bs, 4Cs
• Quintile 4: 8Bs
• Quintile 5: 4A*s, 4As

Most cohorts for whom prior attainment data is presented in this publication will have completed GCSEs prior to the change of GCSE grading from the letter system to the numbered 1-9 system; hence why we present the equivalent letter grade averages for each quintile.

Current Region 

The current region geographical location data is based on the latest address that DWP has recorded for each individual on their Customer Information System (CIS). The LEO dataset does not contain the actual address or postcode for each individual, we currently have data on the Government Office Region (GOR), Local Authority District and Lower Layer Super Output Area (LSOA) where the individual lives at the end of each tax year.  

The CIS is primarily updated when an individual notifies DWP or HMRC of a change of address or through the individual interacting with a tax or benefit system. Individuals who have not been matched to the CIS will not have geographical information. This does not have an adverse effect on the data analysis as ‘unmatched’ graduates are excluded from employment and earnings outcomes.  

For those matched to CIS, address data is available in nearly all cases (over 99.8%), however for those who are not in receipt of benefits or contributing to the tax system then this information could be out of date. Even when contributing to the tax system, employee address is not a mandatory field in the data submitted to HMRC via employers HR systems. It is also possible that in the years soon after leaving university graduates may still use their parents address if they are moving frequently between rented accommodation. More work is needed to try and understand how big an impact this has on the address data held on CIS. 

Home Region

A graduate's home region is found by using their permanent or home postcode immediately prior to starting the course as recorded by HESA.

Residence

Residence information is based on term time accommodation recorded in the HESA student record/the ILR. Note that a student’s residence status may potentially change during their studies; we use their status during their graduating year, this may be different to their residence status in their earlier years. Collection of this variable is mandatory for full-time students and those on sandwich courses; coverage is lower for part-time and other modes of study.

Full Cycle Movement (home region, study region and current region)

Full cycle graduate movement uses three variables (home region – study/provider region – current region) to indicate the migration trend for a student (e.g., “studied in their home region, but currently living elsewhere” or “left their home region to study and currently living in their study region”). 

Due to the way ‘provider region’ is defined it is possible that although studying in a different region to their ‘home region’ some of these graduates were still living in their home region and then commuting to a different region to attend university (e.g. living at home in Sheffield but commuting to a provider in the East Midlands). 

The provider region and home region geographical location variables are both from the HESA student record, and the current region geographical location data is from DWP as is explained in more detail in the above ‘current region’ section.  

If a graduate has an unknown home or current region, they were filtered out of this analysis, meaning that the cohort numbers are smaller than in other breakdowns. An individual may have an unknown home region if their home postcode is not provided by their HE provider, however this only affects a very small proportion of graduates. Reasons for an individual having an unknown current region are explained in the previous ‘current region’ section. We also filter out mature graduates from this analysis because the home region data is unreliable for mature students. This is because the region they lived in prior to starting their course is less likely to be their ‘true’ home region, as they are more likely to have geographically relocated in the years between school and higher education.  

We have presented the full cycle movement breakdowns using five categories in the following format;  

Left home region to study			Stayed in home region to study
Currently in home region	Currently in study region	Currently elsewhere	Currently in home/study region	Currently elsewhere

‘Left home region to study’ means that the graduate attended a HE provider in a region that was not their home region. ‘Stayed in home region’ means that the graduates HE provider was in their home region. These do not define where the graduate lived during their study period (which instead can be seen in the ‘term time residence’ section), because a graduate could move cities for HE but still be within the same region as their home region, or could commute to a different region for HE while still living at home.  

The second row of variables represent the movement between their study region and their current region. If they left their home region to study, the options are that they currently live in the pre-study home region, that they currently live in the region in which they attended HE, or they are living in a different region which is neither their home or study region. If the graduate studied in their home region, they can either currently be in the home/study region (meaning that their home region, study region and current region are all the same), or they could have moved elsewhere and currently be in a different region.  

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Higher Education Graduate Outcomes Analysis

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Contact name: Amy Wilson

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