Introduction
Graduate Labour Market Statistics (hereafter referred to as GLMS) was first published by the Department for Business, Innovation and Skills (BIS) in December 2014, on a quarterly and annual basis. Following a consultation, the publication was reduced to annual only from the April 2016 release. Responsibility for English Higher Education policy, and hence this publication, transferred to the Department for Education (DfE) in Summer 2016.
The GLMS publication uses the Labour Force Survey (LFS) data to look at the labour market outcomes of graduates and postgraduates and compares them with those of non-graduates. Due to the devolution of Higher Education policy, only individuals domiciled within England are considered, though note that English domiciled graduates may have received their degrees from institutions outside England.
This document provides guidance for users regarding the generation and interpretation of figures in the GLMS publication.
Please note, the methodology for the GLMS publication has been updated for the 2022 publication. The main changes relate to:
- the coverage of the graduate, postgraduate and non-graduate groups;
- a reweighting of 2020 and 2021 data;
- the inclusion for the first time of median salaries adjusted for inflation across time series;
- a revision of the suppression policy.
Therefore, statistics produced in the 2022 publication may differ from previous years’ publications, and care should be taken when making comparisons as back series may have changed.
Definitions and Coverage
As of 2022, ONS have introduced new categories to their HIQUAL variable. Detailed mapping of the HIQUAL variable can be found in the LFS Education Derived Variable handbook[1].
The main impact of the amendments to this variable regarding GLMS is the addition of the degree apprenticeship and graduate apprenticeship categories. This, alongside other variable changes, has required a wide-ranging revision to our approach for allocating individuals to the main classification groups of graduates, postgraduates and non-graduates (see below). To ensure consistency across the time series, we have also reviewed allocation across previous years (2007 to 2021) and this has resulted in small changes to the back series.
Graduates, postgraduates and non-graduates
Graduate – defined as individuals whose highest qualification is an undergraduate degree at Bachelor’s level. This category also includes those classified as graduates who are currently enrolled in education courses, including studying towards a Master’s or PhD. As of 2022, this also includes individuals with ‘degree apprenticeships’ or ‘graduate apprenticeships’ equivalent to a first degree.
Postgraduate – defined as individuals whose highest qualification is any degree higher than an undergraduate degree, such as a Master’s or PhD. This category also includes those who have attained a Postgraduate Certificate in Education, as well as those in the category “other postgraduate degrees or professional qualifications”. As of 2022, this also includes individuals with ‘degree apprenticeships’ or graduate apprenticeships’ equivalent to a higher degree. Individuals with a higher degree but “NA” or “don’t know” in the detailed higher education variable in the LFS data (HIGHO) are excluded from this analysis due to uncertainty over qualification.
Non-graduate – defined as individuals whose highest qualifications are at a lower level than an undergraduate degree (National Qualification Framework (NQF[2]) Level 5 or below). This includes individuals with Foundation Degrees, Apprenticeships, A-Levels or GCSEs as their highest qualification, as well as people with lower or no qualifications. It also includes non-graduates who are enrolled on education courses, such as A-level qualified individuals who are at studying at university. As of 2022, undefined nursing and teaching qualifications have also been included within the non-graduate category.
Individuals whose highest qualification is at Level 6, 7 or 8 but is not classified as a degree in the LFS are not considered as part of this analysis.
Where there is ambiguity over the education level of an individual, they are now excluded from this analysis. As a result, the non-graduate group has decreased since the 2021 publication as the number of individuals defined as not in scope, and therefore excluded from the analysis, has increased. Care should therefore be taken when comparing across publications. For a list of qualifications now included in each definition please see Annex A.
It should be noted that the definitions of graduates and non-graduates in Graduate Labour Market Statistics differ from those used in certain other publications which focus on graduate outcomes, such as the Department for Education’s LEO Graduate and Postgraduate Outcomes and HESA’s Graduate Outcomes. The LEO publication refers to graduates as those that graduated with a first degree from an HE provider in England. In contrast, the HESA Graduate Outcomes has much wider coverage and includes those that graduated with at least level 4 qualifications in the UK, but not FE colleges in Scotland or Scottish Alternative Providers. The statistics in this release are not directly comparable with those publications due to the differences in definitions.
Population
The LFS is weighted so that outputs are representative of the general population. In GLMS we use the latest weights for any given year to report outcomes for the specified populations of interest (see ‘Data’ below for more details). Outcomes are presented for two different age groups: the Working Age Population and the Young Population.
Working Age Population – all individuals domiciled in England aged 16-64.
Young Population – all individuals domiciled in England aged 21-30.
GLMS provides a breakdown of outcomes for a ‘young’ sub-population. This is to reflect the fact that the large majority (over 80%)[3] of those that qualified from an undergraduate course in 2020/21 at a UK HE provider (at either NQF levels 4,5 or 6) were under the age of 30, and that new entrants to the labour market are likely to have a limited amount of work experience. Analysis of this sub-group allows comparisons between similarly-aged graduates and non-graduates.
Employment
Employed – individuals that had at least one hour of paid employment in the reference week. This includes individuals that worked both full time and part time.
High Skilled Employment – the proportion of the total specified population who report being in high skilled employment. High-skilled employment is defined as a job categorised within the Standard Occupation Classification (SOC 2020) codes 1-3. SOC 1-3 includes managers, directors and senior officials; professional occupations and associate professional occupations (this is the same definition as used by OfS in their B3 progression metric).
Note that in releases of GLMS prior to 2021, previous SOC classifications were used. The re-classification of SOC in the GLMS 2021 release means that there is a discontinuity in the GLMS series, and care should be taken when comparing with previous publications. For more information on SOC please see the ONS website.[4]
The high-skilled employment rate presented in the GLMS is not the proportion of those employed that are in a high-skilled job; it is the proportion of all graduates that are in a high skilled job. For example, a High-skilled Employment Rate for working age graduates of 70% would mean that 7 out of 10 of all graduates aged 16-64 were in a high skilled job.
Medium/Low Skilled Employment – a job categorised within the Standard Occupation Classification (SOC 2020) codes 4-9. SOC 4-9 includes administrative and secretarial occupations; skilled trades’ occupations; caring, leisure and other service occupations; sales and customer service occupations; process, plant and machine operatives and elementary occupations.
Unemployed – individuals that were not in employment during the reference week and were actively seeking work.
Inactive – also known as the economically inactive, these are individuals that were not employed and did not seek work over the reference period or were seeking work over the reference period, but unavailable to start work (for example graduates undertaking further study).
Employment Rate – the proportion of the total specified population who are employed.
Unemployment Rate – the proportion of the specified economically active population (i.e. in work or unemployed) who are unemployed. This excludes individuals who are economically inactive. Unemployment is defined under the International Labour Organization (ILO) measure, which assesses the number of jobless people who want to work, are available to work and are actively seeking work. This is consistent with the ONS definition of unemployment.
Inactivity Rate – the proportion of the total specified population who report being economically inactive in the labour market.
Proportion of Part-time Workers - the proportion of the total specified population who report that they work part time. The total population is comprised of individuals who report that they work either full time or part time (i.e. not including those who are unemployed or inactive).
Salaries
Populations are specified as above. Median salaries are calculated as the annual equivalent of the weighted median gross weekly earnings of individuals who are in full-time employment, including overtime pay. Salaries only include earnings from individuals’ main jobs (GRSSWK in the LFS). This metric should thus be considered reflective of full-time earning power rather than total income from all jobs.
Individuals working on a part-time basis are not included in the earnings analysis. Salaries are rounded to the nearest £500, in line with HESA’s Graduate Outcomes statistics [5]. The method for calculating the weighted median was adjusted for the 2021 publication and the revised method has been applied for the 2022 publication (see methodology note 2021 for details [6]).
For the first time, annual salaries across the time series have been adjusted to account for inflation and reflect real-time earnings. We publish time series for both nominal and real salaries. For the 2022 publication of GLMS, these have been calculated using the May 2023 edition of ONS Consumer Prices Index including owner occupiers’ housing costs (CPIH) [7]. Adjustment is made to GRSSWK using the quarterly deflators with base 100 set at the calendar year 2007. This inclusion is particularly relevant because of recent inflationary pressures including the impact of COVID pandemic from 2020, and the war in Ukraine from 2022.When looking at 2022 median salaries in isolation (i.e. not in the time series), these are given in nominal terms and are not adjusted for inflation. This includes the data included in the sections Median Salaries: Working-age and Young Populations; 2022 and median salaries by industry.
Coverage
GLMS only covers English domiciled individuals (those whose permanent home is in England) as authority over Higher Education has been devolved to Scotland, Wales and Northern Ireland. Individuals domiciled in England may still have received graduate or postgraduate qualifications from institutions outside England.
Data
GLMS is produced using data from the UK quarterly Labour Force Survey (LFS) – an ONS survey of households living at private addresses in the UK. Its purpose is to provide information on the UK labour market which can then be used to develop, manage, evaluate, and report on labour market policies. For more information on the methodology and quality of the LFS data please refer to the ONS website.[8]
Accuracy: As the results presented in these publications are based on survey data, they represent estimates. Individual estimates may be inaccurate reflections of the true population, and differences between estimates may not be statistically significant. This is particularly true for estimates calculated from small group sizes. To take account of this, we flag where proportions are based on numerators of 30 or less, and particular care should be taken when interpreting these results. See ‘Conversation of quarterly data to annual’ for more information.
Weighting: The LFS collects information on a sample of the population. To enable us to make inferences from this sample to the entire eligible population we must apply weights to the sample data. This entails assigning each responding or imputed case a weight, which can be thought of as the number of people in the population which that case represents. These weights are calculated such that they sum to a set of known population totals, and the weights of an entire dataset will sum to the eligible population of the UK. For further information on LFS weighting, please refer to the ONS User Guidance for the LFS. [9]
Labour Force Survey (LFS) datasets are routinely reweighted in line with population estimates.
- In 2018 a new weighting variable was introduced, PWT18, applying to LFS datasets from July-September 2011 onwards.
- In 2021 a further reweighting was applied to the LFS, with 2020 (PWT20) population weights being applied to both the 2020 and the 2021 LFS quarterly data. This weight was used in the 2021 publication.
- However, in 2022, a further reweighting was applied to the LFS, with 2022 (PWT22) population weights applied to quarterly data since Q1 2020, superseding the previous PWT20 population weights. Care should therefore be taken when comparing across previous publications, as updated weights have been applied for the 2022 publication, meaning outputs for 2020 and 2021 will differ slightly to previous publications. This latest weight uses updated population data to better reflect changes in international migration and other impacts as a result of the coronavirus (COVID-19) pandemic.
The reweighting project completed by the Office for National Statistics has resulted in the following revisions to the GLMS:
- The results from 2020 - 2022 have been calculated using the new 2022 LFS weights.
- The results from July-September 2011 up to 2019 have been calculated using the 2018 LFS weights.
- The results for the years 2006 up to and including April-June 2011 have been calculated using the 2014 LFS weights.
The effect of the change in weighting is generally negligible. For the 2021 publication we found no difference in output larger than 0.1 percentage points in rates (after rounding). For the 2022 publication, the effect of reweighting cannot be isolated from changes associated with the regrouping of graduates, postgraduates and non-graduates, but we expect a similar scale of effect.
GLMS only provides simple outcome measures based on survey data and does not control for the differences in characteristics between graduates, postgraduates, and non-graduates. This means that the outcomes reported may not be wholly attributable to the fact that an individual holds a particular qualification, but instead could reflect other factors, such as regional variations, parental background, gender, innate ability, previous skills and experience or ethnicity.
For more detailed econometric analysis of the earnings and employment differentials between graduates and non-graduates, see research published by the DfE on the absolute[10], [11] and lifetime[12] returns to undergraduate degrees, and previous BIS research on the impact of university degrees on the lifecycle of earnings[13].
The R code used to produce the data for the publication is available for access via GitHub.
Conversion of quarterly data for the annual publication
Respondents to the LFS are interviewed for five successive waves at three-monthly intervals and 20% of the sample is replaced every quarter. Employment status is reported in each wave. All rates in the GLMS (employment rates, high skilled employment rates, unemployment rates, inactivity rates and the proportion of part-time workers) are calculated for each quarter of LFS data, and then the mean of the four quarters is taken to generate an estimate for annual rate, thus accounting for seasonality over the year. Where any of the numerators used for the calculation of quarterly rates are 30 or less, we flag this in the CSV files using ** in the accuracy flag columns. A quarterly rate calculated from such a small group size may be unreliable and could skew the mean for the year. Rates flagged in this way should be treated with particular caution.
Respondents only provide earnings data in waves 1 and 5 of the survey. This means that each individual only reports their earnings once in any given calendar year, as figure 1 demonstrates.
Given that salary data is only collected in one quarter of each given year there is no risk of double counting the salary data of respondents when generating an annual median salary. Therefore, all median salary calculations in the GLMS give the weighted median of all salary data collected for the specified population over the four quarters of data, thus accounting for seasonality over the year. Although the LFS has a slightly larger sample size in Q2, meaning that marginally more weight is placed on salary data in this quarter when calculating the annual median salary, the benefit of a much larger sample size outweighs this small discrepancy. We flag medians that are calculated from 30 or fewer respondents, and these should be treated with particular caution. The median is not susceptible to distortion by extreme values, but estimates may be shifted away from the population median if the group is unrepresentative, and this risk is higher as group size decreases. Medians calculated from 4 or fewer respondents are suppressed for disclosure control.
Graduate breakdown definitions
The GLMS publication provides detail on the employment and earnings outcomes of graduates by their specific characteristics. The breakdowns included are age group, gender, ethnicity, disability status, degree class, subject group (in the data tables only), occupation and industry.
Age groups – 21-30 year olds; 31-40 year olds; 41-50 year olds; 51-60 year olds.
Gender – Male; Female.
Ethnicity – White; Asian; Black; Other. Asian is defined in the Labour Force Survey (LFS) as Asian or Asian British. Black is defined in the LFS as Black, African, Caribbean or Black British. Other combines four groups within the LFS; Mixed or Multiple ethnic groups, Chinese, Arab or Other ethnic group. Other has been combined together as at the disaggregated level the sample sizes were insufficient for robust analysis. The ‘Other’ ethnicity category includes graduates from a wide range of ethnic backgrounds. This category has been included for completeness within the data; however there is likely to be a high level of variation between graduates in this group and caution should be exercised when making comparisons with this group.
Disability status – Disabled; Not Disabled. This breakdown is based on the legal definition found in the Equality Act (2010) which defines a disability as “a physical or mental impairment which has a substantial and long-term adverse effect on your ability to carry out normal day-to-day activities”.
Degree class – First; Upper second (2:1); Lower second (2:2); Third. Data from HESA [14] shows that the percentage of first degree qualifiers from UK HE providers has been changing recently. 28% obtained a first class degree in 2017/18, rising to 36% in 2020/21, but fell to 32% in 2021/22. The proportion of first degree qualifiers with an Upper second was 48% in 2017/18 and fell to 46% in 2020/21.
Occupation –
- High skilled employment (SOC1-3) includes Managers, directors and senior officials (SOC 1); Professional occupations (SOC 2); Associate professional and technical occupations (SOC 3)
- Medium skilled employment (SOC 4-6) includes Administrative and secretarial occupations (SOC 4); Skilled trade occupations (SOC 5); Caring, leisure and other service occupations (SOC 6)
- Low skilled employment (SOC 7-9); includes Sales and customer service occupations (SOC 7); Process, plant and machine operatives (SOC 8); Elementary occupations (SOC 9)
For further information on SOC codes and how these translate to high, medium and low skilled employment with GLMS, refer to the Employment section above.
Industry -
- Agriculture, forestry and fishing
- Banking and finance
- Construction
- Distribution, hotels and restaurants;
- Energy and water
- Manufacturing;
- Other services
- Public administration, education and health
- Transport and communication;
All of these subcategories are given as they are defined in the Labour Force Survey user guide.
Subject Group – Although not published, data on subject group is available in the underlying data. Subjects include: Science, Technology, Engineering and Mathematics (STEM); Law, Economics and Management (LEM); Other Social Sciences, Arts and Humanities. STEM includes: medicine and dentistry; medical related subjects; biological sciences; agricultural sciences; physical and environmental sciences; mathematical sciences and computing’ engineering’ technology; architecture. LEM includes: law; economics; business and financial studies. OSSAH includes: mass communication and documentation; linguistics (English, Celtic and ancient); European languages; Eastern, Asiatic, African, American and Australasian languages and literature; humanities; arts; education. All of these subcategories are given as they are defined in the Labour Force Survey user guide.
Impact of COVID-19 on the labour market
For further information on the impact of COVID-19 on the UK Labour Force Survey, refer to the link to the ONS publication here [15].
Revisions policy
There may be changes to the GLMS statistics produced should there be any changes to the Labour Force Survey questions or coverage, or changes in population weights as recommended by the ONS. Any changes to GLMS will be explained in an update to the Methodology Note.
[2] https://www.gov.uk/what-different-qualification-levels-mean/compare-different-qualification-levels
[3] See Figure 5: https://www.hesa.ac.uk/news/31-05-2023/sb266-higher-education-graduate-outcomes-statistics/activities
[7] https://www.ons.gov.uk/economy/inflationandpriceindices/timeseries/l55o/mm23
[12] https://www.gov.uk/government/publications/undergraduate-degrees-lifetime-labour-market-returns
[14] https://www.hesa.ac.uk/data-and-analysis/students/outcomes