Methodology

Supply of skills for jobs in science and technology methodology

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Defining STEM and occupations most relevant to the critical technologies

Science and technology industries need a wide range of occupations, some requiring high levels of skills and others more administrative. There isn’t a singular definition of the occupations most important to all science and technology industries though it is widely accepted that Science, Technology, Engineering and Mathematics (STEM) jobs are critical to the sector.  

STEM occupations

This publication uses the Royal Society’s definition of STEM occupations to define the workforce. This definition was developed by the Royal Society’s STEM Workforce Classification Group in collaboration with industry and policy experts. It uses the Office for National Statistics (ONS) Standard Occupational Classification (SOC) 2020 at 4-digit. 

The Royal Society “STEM Medicine and Health (M&H)” list encompasses five groups: ‘Science’; ‘Technology and Engineering’; ‘Mathematics’; ‘Medicine and Health’ and ‘Other’ with occupations allocated to the groups based on role tasks. For this report, the ‘Medicine and Health’ occupation group is excluded as it is covered by the NHS Workforce Strategy. The ‘Other’ group is predominantly teachers and is out of scope as teaching staff are a derived demand of other groups and are mainly covered by DfE workforce planning. We refer to the three remaining groups (‘Science’; ‘Technology and Engineering’ and ‘Mathematics’) as “STEM” throughout the publication and dashboard. STEM is a broad group covering a wide range of occupations and skills. 

Note Web Designers were originally excluded from the Royal Society STEM definition and are therefore not included in our analysis. This has now been updated by the Royal Society. 

As with analysing any large diverse group, assessing all STEM workers at once can hide contrasting trends within the group. To begin to mitigate this risk, the report also looks specifically at the jobs deemed most important across five critical technologies: Artificial Intelligence, Engineering Biology, Quantum Technologies, Future Telecommunications and Semiconductors. 

Occupations most relevant to the critical technologies

This publication also looks in detail at a group of occupations most relevant to the five critical technologies set out in the Science and Technology Framework as having strategic importance to the UK. These are Artificial Intelligence, Engineering Biology, Quantum Technologies, Future Telecommunications and Semiconductors. 

Through this work, both a long list and short list of occupations relevant to the critical technologies were created. This report looks at the short list of the occupations identified as most relevant to each of the critical technologies. These lists were created with expert technology policy leads in the Department for Science, Innovation and Technology (DSIT). Each established a long list of occupations relevant to their priority technology, forming a total combined list of 91 relevant occupations to the critical technologies. The most relevant occupations to each technology were then assigned by the technology policy leads, forming a short list of 32 occupations most relevant to the critical technology occupations. Shortlists were identified using expert internal advisors, stakeholder engagement across sector and skills leads and the existing published and unpublished evidence base.

The combined short-list is what is referred to as “Critical Technologies” throughout the publication.

Occupations most relevant to the critical technologies are not exclusively relevant to the critical technologies: workers in these occupations will be in roles across the whole UK economy, not just in critical technology industries. Furthermore, critical technology industries will require skills and knowledge to be developed in a broader selection of both non-STEM and STEM jobs not included in the list of jobs most relevant to the critical technologies. 

Occupations most relevant to digital and computing

This publication also looks in detail at a group of occupations most relevant to digital and computing. This list of fourteen occupations was identified by the Digital and Computing Skills Education Taskforce in 2023. 

Note: full occupation classifications are available to download in supporting files of the publication.

Detail on the critical technology definitions

Critical Technologies
AI

Artificial intelligence (AI) - is one of the five critical technologies of strategic importance to the UK as stated in the Science and Technology Framework. It refers to machines that perform tasks normally performed by human intelligence, with machines demonstrating autonomy to operating in fast-moving environments by adapting and automating complex cognitive tasks.

AI policy leads at DSIT identified 18 occupations of relevance to AI which fed into the long list of occupations relevant to the critical technologies. They then identified 10 occupations from this long-list which are most relevant to AI – these form part of the short list of occupations most relevant to AI and to the critical technologies.

Engineering Biology

Engineering Biology is one of the five critical technologies of strategic importance to the UK as stated in the Science and Technology Framework. This is the application of rigorous engineering principles to the design of biological systems.

Engineering Biology policy leads at DSIT identified 29 occupations of relevance to Engineering Biology which fed into the long list of occupations relevant to the critical technologies. They then identified 12 occupations from this long-list which are most relevant to Engineering Biology – these form part of the short list of occupations most relevant to Engineering Biology and to the critical technologies.

Quantum

Quantum technologies is one of the five critical technologies identified by quantum policy leads at DSIT. These mechanics are used to unlock new technological advances in areas such as sensing, imaging, communications, timing and computing.

Quantum policy leads at DSIT identified 25 occupations of relevance to Quantum which fed into the long list of occupations relevant to the critical technologies. They then identified 11 occupations from this long-list which are most relevant to Quantum – these form part of the short list of occupations most relevant to Quantum technologies and to the critical technologies.

Semiconductors

Semiconductors is one of the five critical technologies of strategic importance to the UK, as stated in the Science and Technology Framework. These are a class of electronic materials with unique properties that are used to create the hardware which underpin electronic devices.

Semiconductors policy leads at DSIT identified 66 occupations of relevance to Semiconductors which fed into the long list of occupations relevant to the critical technologies. They then identified 10 occupations from this long-list which are most relevant to Semiconductors – these form part of the short list of occupations most relevant to Semiconductor technologies and to the critical technologies.

Future Telecommunications

Future Telecommunications is one of the five critical technologies of strategic importance to the UK as stated in the Science and Technology Framework. This involves evolutions of the infrastructure for digitised data.

Future Telecommunications policy leads at DSIT identified 47 occupations of relevance to Future Telecommunications which fed into the long list of occupations relevant to the critical technologies. They then identified 12 occupations from this long-list which are most relevant to Future Telecommunications – these form part of the short list of occupations most relevant to Future Telecoms technologies and to the critical technologies.

Current STEM workforce

Estimates on numbers currently working in STEM and occupations most relevant to the critical technologies are taken from the Annual Population Survey (APS) July 2022-June 2023. These figures form the base of our future estimates. These are at SOC2020 (4 digit). 

Future workforce projections

The future employment estimates are taken from the 2035 Skills Imperative projections (opens in a new tab), produced by NFER / Warwick Institute of Employment.  These projections were released in 2022 and are part of a long running series of projections produced every 2-3 years (previously under the title Working Futures). These include employment projections at detailed SOC 2020 (4 digit) allowing us to identify STEM and critical technology occupations.

These projections were found to be most appropriate for this style of analysis in a recent report commissioned by the Department for Education: Future skills projections and analysis - GOV.UK (www.gov.uk) (opens in a new tab).

We take two scenarios: 

  • A baseline scenario that continues forward trends in jobs across different industries
  • A technological opportunities scenario that makes assumptions around the faster adoption of new technologies.

For each scenario we take the growth rates from the forecasts and re-base to the latest employment numbers from the Annual Population Survey (July 2022-June 2023).

We have produced two further simple scenarios to show the uncertainty. This estimate future STEM employment based purely on population growth and employment under a high STEM employment growth scenario (+10% to 2030).

Modelling future employment

Future employment numbers are projected by combining employment rates from the Annual Population Survey (APS) with ONS population projections. Employment rates are considered by sex and single year of age and are assumed to stay constant at the levels observed in the latest data for all years of the projection period.

Full assumptions feeding into the future projection scenarios can be found here: https://www.nfer.ac.uk/key-topics-expertise/education-to-employment/the-skills-imperative-2035/publications (opens in a new tab).

Education pathways

Data sources

The education pathways analysis is based on ASHE-LEO data; a new data resource available in the Department for Education. It brings together the longitudinal education and labour market information in the Longitudinal Education Outcomes study (LEO) with the information on employment and earnings in the Annual Survey of Hours and Earnings (ASHE) survey. 

There are around 100k individuals in the ASHE-LEO sample in each year. This represents 45-75% of the overall ASHE sample, with later years having a better match rate than earlier years, and younger ages having a better match rate than older ages. ASHE-LEO is used here as a sample of early career employees in LEO (employees aged 23-30 in the 2018-19 tax year). 

Coverage

The data covers employment in England across the 2016-17 to 2018-19 tax years. It focuses on young adults and includes details of their education achievements spanning GCSEs and post-16 learning. The ASHE-LEO data is based on a sample of employees meaning that the results are based on those employees in LEO who are also included in the ASHE sample.

Cohort selection

The analysis includes individuals in LEO who turned age 23-30 in 2018-19, were schooled in England, and have education records in the National Pupil Database. Individuals must also meet the following criteria to be included:

  • Matched successfully to the ASHE survey.
  • Not studying in a Higher Education institution in the year employment is measured.
  • Living in England in the year employment is measured.

There are important differences between the cohort used for this analysis and the wider population of employees in England:

  • The cohort is not representative for employees of all ages. It is focused on young adults born between September 1988 and August 1995.
  • The cohort is restricted to employees who were schooled England. This means employees who went to school elsewhere in the UK or who came to the UK as migrants are not covered.
  • The data matching used to generate the ASHE-LEO and the use of the raw unweighted ASHE sample may introduce uncertainties in coverage of the cohort.

To boost the sample size up to 30k, the cohort is selected across three years of the ASHE survey, 2017, 2018 and 2019. These years cover employment in the 2016-17 to 2018-19 tax years respectively. Where employees are found in multiple surveys, we take the latest year, meaning that the majority of the sample is based on 2018-19 data (70%), with smaller percentage in 2017-18 (20%) and the smallest in 2016-17 (10%). This means that a small number of students (fewer than 5%) were aged 21 or 22 when employed in 2016-17 or 2017-18. All students were aged 23-30 in 2018-19.

Highest qualification

Employees achieve a range of qualifications before entering work. This analysis identifies the highest qualification achievement of employees at the time their employment activity is measured in the ASHE survey. This qualification is used to summarise the education pathway of employees. The hierarchy used to select between qualifications is below:

  • Highest qualification.
  • Most recently achieved qualification (if achieving more than one at the same level).
  • If the same record appears in the ILR and HESA collections, the HESA record is selected over the ILR record. 

The hierarchy used to determine highest qualification is:

  • Doctorate
  • Other level 8
  • Master’s degree
  • Other level 7
  • First degree
  • Higher apprenticeship level 6
  • Other level 6
  • Foundation degree
  • Higher apprenticeship level 5
  • Other level 5
  • Higher apprenticeship level 4
  • Other level 4
  • Advanced Apprenticeship
  • Full Level 3 (including academic qualifications, e.g. A-Levels)
  • Other Level 3
  • Intermediate Apprenticeship
  • Full Level 2 (including academic qualifications, e.g. GCSEs)
  • Level 2 English and Maths
  • Other Level 2
  • Entry or Level 1 English and Maths
  • Other Entry or Level 1
  • Unassigned

Defining pathways

Education pathways are split into once of the following categories based on the highest qualification held by the employee:

  • HE – all employees who achieved their highest qualification in the Higher Education (HE) sector (all information is sourced from the HESA student record).
  • Apprenticeships – all employees who achieved an apprenticeship as their highest qualification (all information sourced from the Individualised Learner Record (ILR)).
  • FE – employees who achieve a classroom qualification in FE excluding A-Levels and GCSEs (all information sourced from the ILR).
  • A-Levels – employees who achieved A Levels as their highest qualification (sourced from the ILR and the school census).
  • GCSE – employees who achieved GCSEs as their highest qualification (sourced from the ILR and the school census).
  • Other – employees not classified elsewhere.

The pathway distinctions are subjective, and it would be plausible to draw the distinctions in a different way. For example:

  • HE qualifications achieved in the FE sector are here classified as FE.
  • A-Levels achieved in the FE sector are classified separately to other FE qualifications.

Defining subject area

The subject areas of qualifications are different for the HE pathway compared with all other pathways:

  • HE subjects are recorded using Joint Academic Coding System (JACs).
  • All other pathways are categorised using the Sector Subject Area tier 1 (SSA) classification system.

We do not attempt to map the differing subject categories into a single classification system.

A-Level and GCSE pathways are not mapped to subject areas as they contain bundles of qualifications that may not align to a single subject area.

Defining STEM pathways

We classify FE and apprenticeship qualifications as STEM where they are classified into the following SSAs:

  • Engineering and manufacturing technology
  • ICT
  • Science and maths
  • Construction, planning and the built environment

For HE qualifications, we use the JACs science marker published by HESA: https://www.hesa.ac.uk/support/documentation/jacs (opens in a new tab) 

These definitions are not consistent, and this will contribute to any differences observed when analysing STEM subject areas for the FE pathway (predominantly education levels 2-3) and the HE pathway (levels 4+).

Measuring employment activity and occupation.

Occupation is based on the Standard Occupational Classification 2010 (SOC 2010) and is measured using the main job for each employee captured in the ASHE survey. 

Where an employee has more than one record in the ASHE surveys between 2016 and 2019, then we take:

  • The latest record (i.e. 2019 ASHE is preferred over 2018 ASHE)
  • Where the records are in the same year, the record with the most hours worked.

Skills demand

Information included in the dashboard (opens in a new tab) on skills demand comes from the Employer Skills Survey 2022 (ESS 2022). This gathered labour market intelligence (LMI) on employer skills needs and training activity among employers in England, Scotland, Northern Ireland and Wales. It is the sixth in the biennial series of Employer Skills Surveys dating back to 2011. For the core ESS 2022 survey, a total of 72,918 interviews were undertaken between June 2022 and March 2023.

In the occupational data each row represents one occupation for which an establishment had at least one vacancy, at the time of the survey. There are cases in which two or more occupations for a specific establishment have been coded to the same SOC code. When calculating the base sizes for each SOC code, this was taken into account. The base sizes should then be intended as the number of occupations for which establishments had at least one vacancy in the specific SOC code.

You can find out more about the ESS 2022 methodology: Employer Skills Survey 2022, Methodology – Explore education statistics.

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