Author
Listed:
- Zhaolu Liu
- Jonathan M Clarke
- Bertha Rohenkohl
- Mauricio Barahona
Abstract
A job usually involves the application of complementary or synergistic skills to perform the required tasks. Such relationships are implicitly recognised by employers in the skills they demand when recruiting new employees. Here we construct a skills network based on their co-occurrence in a national level data set of 65 million job postings from the UK spanning 2016 to 2022. We then apply multiscale graph-based community detection to obtain data-driven clusters of skills at different levels of resolution that reveal modular groupings of skills across scales. The obtained skill clusters occupy different roles within the skills network: some have broad reach across the network (high closeness centrality) while others have higher levels of within-cluster containment. Yet there is high interconnection across clusters and no skill silos. Furthermore, the skill clusters display varying levels of within-cluster semantic similarity, highlighting the difference between co-occurrence in adverts and intrinsic thematic consistency. The skill clusters are characterised by diverse levels of demand, with clear geographic variation across the UK, broadly reflecting the industrial characteristics of each region, e.g., London is an outlier as an international hub for finance, education and business. Comparison of data from 2016 and 2022 reveals increasing employer demand for a broader range of skills over time, with more adverts featuring skills spanning different clusters. Our analysis also shows that data-driven clusters differ from expert-authored categorisations, suggesting they may capture relationships between skills not immediately apparent in expert assessments.Author summary: Jobs often require employees to apply a wide range of skills in their work. Understanding how these skills relate to one another is important to provide insight into how employees may be more or less able to carry out their jobs or find other jobs, as well as to track how occupations change over time, for instance when new technologies are introduced. In this study we use a large data set of 65 million job adverts between 2016 and 2022 across the whole of the UK to examine the patterns of skills required together by employers. We find clusters of skills that appear together in adverts often, but these clusters do not always agree with how experts group skills based on competencies or qualifications. Overall, we find a strong co-requirement of varied skills by employers, with less interconnection for some technical skills, such as in cybersecurity. Which skill clusters are in demand varies significantly across the UK, with London standing out as an international hub for finance, education and business. Over time, skills in the UK labour market have become more interconnected, reflecting employers expecting workers to possess a more diverse range of skills to do their jobs.
Suggested Citation
Zhaolu Liu & Jonathan M Clarke & Bertha Rohenkohl & Mauricio Barahona, 2025.
"Patterns of co-occurrent skills in UK job adverts,"
PLOS Complex Systems, Public Library of Science, vol. 2(2), pages 1-25, February.
Handle:
RePEc:plo:pcsy00:0000028
DOI: 10.1371/journal.pcsy.0000028
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcsy00:0000028. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: complexsystem (email available below). General contact details of provider: https://journals.plos.org/complexsystems/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.