‘Skills Match is an interactive website for different types of people and organisations interested in connecting young Londoners to London’s jobs. It is an online resource that allows an exploration of the dynamic between skills supply and employer demand (at Level 3 and below) in London.’
Skills Match was funded through a successful bid to the Open Data Breakthrough Fund from the Department for Business, Innovation and Skills and the Cabinet Office. The development of Skills Match was brought about by the need to address specific issues, namely: youth unemployment; skills shortages/deficits; skills mismatches; and the lack of good, local skills supply data.
The target audience for Skills Match is careers advisers, schools, colleges, planners, policy makers and employers.
Skills Match provides analysis of skills gaps (expansion and replacement demand) and trends (projected jobs over time) in a visually engaging way.
Summary of analysis provided by Skills Match
The Skills Match website provides analysis of:
Skills Match uses LMI for All (principally Working Futures) to drive the analysis of likely trends in the London labour market by occupation and qualification level. Other datasets that contribute to Skills Match are:
Click here to see screenshots of LMI for All working on Skills Match.
As Skills Match was funded through the Open Data Breakthrough Fund (made available to enable open data release and ease of access to open data), Skills Match was developed largely with open data sources, including LMI for All.
Critical to the development of Skills Match was linking and standardising skills and labour market datasets. LMI for All had already connected and standardised existing sources of labour market information, so it was an ideal dataset to contribute to the development of Skills Match.
Additionally, LMI for All provided access to an extensive set of labour market information and was specifically geared towards applications designers that were attempting to address transitions issues into and through the labour market.
The project team set out the questions that needed to be tackled, that is:
Based on the above, MIME Consulting built a multi-levelled theoretical data model then looked at different data sources to match the model. The team initially explored two different, existing jobs data feeds, but the way the data was codified was not useful, and there were data quality issues.
With LMI for All, the team found that the way the data was codified could be more easily matched to the skills data that was being used from the National Pupil Database and the Individual Learner Record, although it was still a significant job to make the match.
As there was no pre-existing mapping of subjects to occupation codes, the team had to produce its own mapping of Sector Subject Area (SSA) 3 course codes to Standard Occupational Classification (SOC) 4 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 mapped to multiple occupations. Where one job code mapped to more than one subject area, the demand for jobs was 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.
When exploring LMI for All, the team found that ‘region’ was the lowest level of available data, yet local authority level data was key to the Skills Match specification. To address this, the team used zone information available in the 2011 census.
GLA Economics data (for overall job projections and replacement demand) was also used as it provides London level analysis that takes account of the London context (for example, housing and population growth), which does not feature in nationally produced datasets. The team also needed to ensure that Skills Match complemented the London economy ‘story’ that the GLA Economics analysis sets out.
Forecasting analysis tools have to tread the line between art and science with the skill of a mountain goat, so data credibility is essential.
LMI for All provided the team with comprehensive, robust and nationally recognised data sources, which in turn provided Skills Match with credibility.
The team would like to apply the next wave of UKCES (Working Futures) modelled data to the site (and update the latest skills data from the Department for Education and the Skills Funding Agency).
The team is also interested in modelling higher level data for jobs at Level 4 and above using Higher Education data in addition to LMI for All.