Open Jobs
www.nesta.org.uk/project/open-jobs/
www.nesta.org.uk/project/open-jobs/
Open Jobs is a programme of work that focuses on improving labour market information. The programme shows how cutting-edge data science methods, when applied to large sources of text data, can provide new insights on a range of labour market issues, including skill shortages, job quality, automation risk and green jobs.
Each project within the Open Jobs programme was funded externally and the team are extremely grateful for support from the following organisations: the Department of Education, the Gatsby Foundation, the JP Morgan Chase Foundation, the Economic and Social Research Council, the Economic Statistics Centre of Excellence, the Productivity Insights Network and SkillsFuture Singapore.
The outputs of the programme included research papers, open tools, interactive data visualisations and reports.
We have developed some example use cases that can be tackled using our tools:
You can access the following tools below:
This paper is Nesta’s second data-driven skills taxonomy. The improved method does not rely on a predetermined list of skills, and can instead automatically detect previously unseen skills. The resulting taxonomy contains three levels and 6,685 separate skills. It could be used as a base for the first UK-specific skills taxonomy, to spot regional skill clusters, and for rapid assessments of skill changes following shocks such as the Covid-19 pandemic.
The Observatory is where Nesta’s collection of online job adverts are stored. Nesta started gathering online job adverts in 2021, collecting adverts on a daily basis. As of mid 2024, the Observatory contained over 8 million UK online job adverts. This article illustrates new insights about skills that can be unearthed from these advertisements.
Nesta’s first research paper on skills taxonomy shows how job adverts can be used to create a data-driven taxonomy that is independent of expert-derived taxonomies. The paper illustrates how the taxonomy can be automatically and quickly updated to reflect changes in labour demand, providing timely insights to support labour market decision-making.
This research paper explores how workers’ skills vary across the UK. The analysis uses a purchased dataset of 53 million job adverts, combined with Nesta’s first skills taxonomy.
This article outlines some key findings from the Green Jobs explorer and provides ideas for how the tool can be used.
This technical article explains how green skills, mentioned within job adverts, are automatically identified and extracted. It also presents high-level analysis of green skills across the UK. Green skills are just one of three dimensions that are measured within the Green Jobs Explorer. There is also a technical article describing how we extracted SOC codes and one on how we extracted SIC codes from job adverts – algorithms pivotal to our two other dimensions of greenness
This article, published in Recruiter magazine, explains how the lack of monitoring and information on green skills threatens to slow the transition to net zero. It argues that this ‘data gap’ takes the pressure off sectors sitting outside the ‘green’ economy, risks hampering workers and job seekers from driving change, and prevents policymakers from identifying regions or sectors where firms may be struggling to adapt.
This research paper uses millions of online employee reviews, from Indeed’s UK website, to enhance our understanding of job quality. Keywords within the reviews are extracted and then clustered to form a taxonomy of job quality. It is the first UK taxonomy that is derived from employee reviews and the first to be updatable in real time.
This project provides a method of analysing dimensions of job quality as they appear in online job advertisements. We extracted and classified a range of aspects mentioned within the job adverts, including employment terms (eg, offers to work flexible hours), benefits (eg, life assurance) and job design (eg, mentions of career progression). Two data stories were also produced, one focussing on offered job quality in the UK, and the other on job quality in adverts for early-years practitioners.
In this report, machine learning is used to create a ‘map of the labour market’ that captures the similarities between over 1,600 jobs, based on the skills and work activities that make up each role. The resulting map shows how a worker's skills and experience can be transferred to a range of nearby jobs.
This demonstrator tool allows a user to find occupations that are similar to their current role (in relation to the skills that their role requires). The tool leverages the algorithms developed in Mapping Career Causeways and aims to show how they can help career advisors recommend suitable career paths to their advisees. This technical article describes how the demonstrator works in more detail.
Shortly before Covid-19 shook the world, Nesta began a collaboration with SkillsFuture Singapore to extract novel insights from their Skills Frameworks. These frameworks detail the competencies required in almost 1,500 job roles, covering 30 different sectors in Singapore. This article showcases the findings and puts forward the case for developing a skills framework in the UK.
This report compares the automation risk among UK apprenticeships and identifies the drivers of risk. Specifically, it estimates the suitability of the tasks within an apprenticeship for machine learning. The estimates of automation risk come from a US study by Brynjolfsson, Mitchell and Rock. The US occupations are then mapped to UK apprenticeships.
