A New Approach to Building a Skills Taxonomy

Elizabeth Gallagher (), India Kerle, Cath Sleeman and George Richardson

No ESCOE-TR-16, Economic Statistics Centre of Excellence (ESCoE) Technical Reports from Economic Statistics Centre of Excellence (ESCoE)

Abstract: This paper presents a new data-driven approach to building a UK skills taxonomy, improving upon the original approach developed in Djumalieva and Sleeman (2018). The new method improves on the original method as it does not rely on a predetermined list of skills, and can instead automatically detect previously unseen skills. This 'minimal judgement' approach is made possible by a classifier that automatically detects sentences within job adverts that are likely to contain skills. These 'skill sentences' are then grouped to define distinct skills, and a hierarchy is formed. The resulting taxonomy contains three levels and 6,685 separate skills. The taxonomy could be used as a base for developing the first UK-specific skills taxonomy, which domain experts would then refine and extend. It could also be used to spot regional skill clusters, and for rapid assessments of skill changes following shocks such as the COVID-19 pandemic.

Keywords: big data; cluster analysis; job market; labour demand; machine learning; nlp; online job adverts; sentence embeddings; skills; skills taxonomy (search for similar items in EconPapers)
JEL-codes: C18 C38 J23 J24 (search for similar items in EconPapers)
Date: 2022-05
New Economics Papers: this item is included in nep-big and nep-lma
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