Methodology

The data used to produce the data visualisations are available for download.

Download CSV data file Download data dictionary

Data collection

Demand data

Burning Glass has collected more than 1.5 million jobs posted online by employers in the UK since 2012. Burning Glass uses advanced natural language analytics to turn the information in each job posting into usable data. This allows Burning Glass to describe employer demand for specific roles or skills.

The demand for entry-level (< 2 years of experience) talent is compared with the available supply of new graduates or trainees. Burning Glass postings data is normalised against vacancy data published by the Office for National Statistics (ONS) and Jobcentre Plus. The data is further validated against the Annual Survey of Hours and Earnings (ASHE) from the ONS.

Supply data

We have used the numbers of learners leaving higher and further education (programme finishers by subject area) as a proxy for the ‘supply’ of entry-level talent.

Supply data are sourced from the following agencies:

Occupations

We use the standard occupational classification 2010 (SOC2010) by the ONS, which is the official classification of occupational information for the UK. Within this system, jobs are classified in terms of their skill level and skill content. In this tool, the occupations are shown at ‘minor group’ or 3-digit level.

Updating data

Data on this website is from 2014. Burning Glass and IPPR hope to refresh this tool with new data each year. It may also be possible to supplement the existing data with further datasets and information, to provide background, contextual information or data on other learners not included in the existing further and higher education datasets.

Analysis

Linking supply and demand data

IPPR and Burning Glass undertook this comparison by building a customised confluence or ‘crosswalk’ between degrees, college courses and jobs. The crosswalk uses destination data from national surveys to inform the likely career trajectories of programme finishers, informing the most nuanced and accurate mapping of how education programmes align to specific jobs.

Normalisation methodology

Burning Glass postings data are first normalised against the number of total vacancies as published in the ONS Vacancy Survey, to ensure that the total level of demand is estimated correctly. The Burning Glass data are then further normalised using information from the Jobcentre Plus vacancies, provided by the ONS's Nomis service from ONS. In this way we make sure that the geographic distribution of demand and the distribution of occupations are correctly represented by the normalised demand.

In order to validate this normalisation method, the distribution of normalised demand is compared with the distribution of employment, which is estimated by the ASHE survey from the ONS. The comparison is done by geography and by major occupational group. Correlation of the two distributions is very high.

Job concentration

Job concentration, also known as a location quotient, is the number of job openings per capita in a region or LEP area. For each combination of occupation and location, we take the ratio of job posting for entry-level positions to local employment. This ratio is then normalised using the ratio of total UK entry-level postings for the same occupation and the UK employment total. The formula can be represented as ‘location quotient by occupation and geography = (local entry-level postings/local employment) / (national entry-level postings / national employment)’.

Advertised salary

Burning Glass salary data is based upon the salary stated in a job posting. When a range is specified, the mid-point salary is used. Different salary frequencies are adjusted to reflect an annual salary. Salary is shown by location at the nation or region level.

Using full time employee wages from the ASHE survey data, the 20th, 40th, 60th and 80th percentiles were used to determine the wage categories of ‘very low’, ‘low’, ‘medium’, ‘high’, and ‘very high’. As the occupations shown in the tool are mid-skilled (excluding elementary and manual roles), there are very few in the lower categories.