Frequently Asked Questions

London Councils’ Young People’s Education and Skills (YPES) Board identified that a more rational, informed and managed process was required to ensure the skills of young people match the requirements of employers and the London economy. Youth unemployment is a major issue affecting London, in which 24.7% of young people are unemployed (compared to 20.9% nationally).

Skills Match was developed in order to address the current skills mismatch between job seekers and the labour market, by bringing together disparate datasets in a easy to use manner.

Skills Match was developed by MIME Consulting, educational data specialists, in conjunction with London Councils. The project was funded through the Open Data Breakthrough Fund.

The development of Skills Match was informed by a steering group comprising numerous practitioners from Local Authorities, the Greater London Authority and careers organisations.

The primary users of Skills Match are intended to be:

• Careers advisors – To use in advising young people on growth sectors (and therefore courses that will maximise their chances of getting employment)
• Education/training providers/curriculum planners – To ensure that the courses put on reflect future demand for skills.
• Policy makers / planners – To ensure that the type of provision required across London is appropriate for future skills requirements.
• Employers – To identify the education providers that are providing the talent they require to fulfil their skills gaps.

Supply refers to the number of London residents from state funded education (i.e. maintained schools, academies and colleges) projected to successfully complete a course when aged 17 or 18 (and therefore assumed to be ready to enter the labour market).

The analysis does not include learners who only studied a subject categorised as “Preparation for Life and Work” (which covers courses such as basic literacy) or “IT for Users” because they cannot be linked to a specific occupation.

Note that some learners would be counted as supply in more than one year. This is because some learners successfully complete a course aged 17, then complete a different course aged 18. We count these learners as being available to the labour market at both 17 and 18.

Note that in Skills Match you can choose to remove a proportion of learners on Level 3 courses from the supply figures because they can be assumed to have always intended to move into Higher Education, rather than joining the workforce at age 18. This option is available as an option in both the Skills Gaps and Employer Demand and Skills Supply Map sections.

Demand refers to the number of projected vacancies in London that are not going to be filled by people already in employment (i.e. the demand needs to be met by people leaving education or other new entrants to the London labour market). Demand is comprised of new jobs created in growing industries, plus replacement demand (for example where someone retires from a job).

Important: Demand for labour met by those already in employment is not included in Skills Match, and therefore the number of projected vacancies shown will be far less than the actual number of vacancies.

As you would expect from a project funded through the Open Data Breakthrough Fund, Skills Match was developed largely with open data sources (including data readily available as well as data that required a specific data request). The main sources were as follows:

Supply Side:

• DfE National Pupil Database – For information on young people completing post-16 courses in schools
• Skills Funding Agency/Data Service ILR – For information on young people completing 16-18 courses in colleges and apprenticeship providers

Demand Side:

• GLA Economics – For overall job projections, and information on replacement demand and net requirement from education/new entrants (see: http://www.london.gov.uk/sites/default/files/llmp.pdf)
• UKCES Working Futures – For detailed jobs data and qualification levels
• 2011 Census (Workplace Zones) - For the distribtion of jobs between different Local Authorities in London (because the Working Futures data is London level only)

Subjects are presented at two levels in Skills Match.

The first is the top-level Sector Subject Area (“SSA1”) commonly used in education (e.g. by Ofqual). There are 15 SSA1 codes, however, Skills Route does not present SSA14 (Preparation for Life and Work) as this is not sector specific.

You can drill-down through SSA1s into a more detailed subject level. We term the second level of subject code used in Skills Match “SSA3”. This is a set of around 130 codes developed specifically for Skills Match which groups together individual subjects, to provide a more relevant link to occupation codes. Around 99% of all course enrolments in London were mapped to one of the SSA3 codes.

Note that there is a second official level of sector subject areas (“SSA2”) but this was not considered granular enough for users of Skills Match.

Many 16-18 learners will be studying more than one course, and these will often span more than one subject area. In order that we don’t double count learners, we use the concept of full-time equivalent (FTE). This means that a learner studying, say, 3 A-Levels in the Science and Maths SSA, and 1 in Social Sciences would be counted as 0.75 FTE in Science and Maths, and 0.25 in Social Sciences.

Skills Match breaks down analysis by the NQF (National Qualification Framework) qualification level of the learner. The NQF levels used are as follows:

• Level 3: A-Level and equivalent
• Level 2: Equivalent to A*-Cs at GCSE
• Level 1: Equivalent to D-Gs at GCSE
• Entry: Below level 1

UKCES Working Futures data breaks down the number of jobs by the qualification level of those in employment, and this is used to project vacancies by qualification level in Skills Match. The qualification levels of vacancies are therefore assumed to be in the same proportion as people actually employed in that occupation at the national level.

The data for number of vacancies is based on the Standard Occupational Classification (“SOC codes”) used by the ONS. There are 369 4 digit SOC codes which enable us to categorise information on job vacancies (although not all of these are available to learners at certain qualification levels). Each of these SOC codes was matched to one or more SSA3 code.

Note: There is another method of categorising jobs, by industry: Standard Industrial Classification or SIC codes. It was decided not to use this categorisation for the purposes of Skills Match because the school/college based course required for a particular occupation in one industry is usually the same for that occupation in a different industry (e.g. someone completing an Accountancy course could start a career as an Accountant in a Marketing company, or in a bank). Industry specialities tend to be developed later on in a career.

As there was no pre-existing mapping of subjects to occupation codes, we had to produce our own mapping of SSA3 course codes to SOC4 occupation codes. This was done on the basis of word similarities, as well as research into the nature of particular courses. Since there are 369 SOC codes and only 130 SSA3 codes, most subjects map to multiple occupations.

Where one job code mapped to more than one subject area, the demand for jobs has been apportioned evenly between subject areas (in practice this will be an oversimplification, but there is no hard data available showing exactly how the codes should be apportioned).

The matching, by its very nature, is subjective and imperfect. If you have any suggestions for improvements, please let us know by emailing skillsmatch@mimeconsulting.co.uk.

The demand side (job vacancy projections) uses UKCES modelling. For details, please see:
https://www.gov.uk/government/publications/working-futures-2012-to-2022

For the supply side (courses completed) data, we look at historical trends for the last three years, and project these forward using logarithmic projections.

You can download the underlying data behind each of the visualisations in Excel format by clicking the buttons underneath the analysis, or via this link.

It is intended that the data will be updated on an annual basis.

Furthermore, the matching algorithm between subjects and jobs may be improved once more data becomes available; for example, by using datasets that show what jobs people studying a particular subject actually go on to.

As with any modelling project, there are clearly a number of key caveats to consider when looking at the analysis provided by Skills Match. Some of the key points to consider are as follows:

1. Data sources used: The quality of any modelling is only as good as the raw data it is built on. While the skills supply data is based on actual number of learners (albeit projected into the future), the job demand data is based on assumptions made around economic growth and a range of other variables that are notoriously hard to predict.
2. Linking between course subjects and occupations: Many vocational subjects specifically train young people to become employed in particular occupations. On the other hand, many academic subjects are intended to lead to further study. Skills Match links all learners to an occupation based on a best fit approach; in practice learners may be qualified to start a wide range of jobs not directly linked to their course. This is particularly relevant because Skills Match does not currently include university or other higher level qualifications.
3. Levels of provision: At the time of production, there was no readily available open dataset showing the number of London residents qualifying from university, split by subject. Hence, Skills Match only looks at skills provision at Level 3 and below, and matches to projected job demand at these levels. Skills Match does not take account of learners that have completed a 16-18 course and are now moving on to a higher level course (for example at university ).For context, the latest DfE destination measures for 2011-12 suggest that at least 62% of Level 3 learners from London will go on to have a sustained destination at a UK Higher Education Institution (for more information, see: https://www.gov.uk/government/publications/destinations-of-key-stage-4-and-key-stage-5-pupils-2011-to-2012)
4. Inward and outward commuting: Skills Match takes no account of the fact that young people resident in London may choose to work outside of London, or that people living outside of London may choose to move into or commute into London to meet some of the job demand. For context, in the 2011 census around 63,000 16-24 year olds commuted into London from outside, and 34,000 commuted out (the net effect of this would be an increase in around 8% to the “supply” of labour). For more information please see: https://www.nomisweb.co.uk/census/2011/origin_destination
5. Inward migration into London's labour market: In practice, some of the demand for new workers may be met by migrants into the London economy, both international and from other regions of the UK. This may be particularly true in certain sectors (e.g. construction). For reference, the GLA note that over 150,000 international migrants moved in London each year, as well as 150,000 people relocating from elsewhere in the UK, based on data from the last 10 years (see: http://www.london.gov.uk/sites/default/files/llmp.pdf)
6. Forecasting: Any forecasting model is prone to error, and all projections should therefore be treated with caution.

We are keen to hear from other organisations and individuals who have suggestions for ways of dealing with some of these limitations, so please do get in touch by emailing skillsmatch@mimeconsulting.co.uk.

This presentation has been put together to give an broad overview of the methodology

If you have further questions please do get in touch by emailing skillsmatch@mimeconsulting.co.uk.

We welcome any comments on data anomalies found or suggestions for adapting the methodology. Please email us at skillsmatch@mimeconsulting.co.uk, or we would be grateful for your comments via our feedback survey.