Classifying Occupations According to Their Skill Requirements in Job Advertisements

Jyldyz Djumalieva (), Antonio Lima () and Cath Sleeman ()

Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)

Abstract: In this work, we propose a methodology for classifying occupations based on skill requirements provided in online job adverts. To develop the classification methodology, we apply semi-supervised machine learning techniques to a dataset of 37 million UK online job adverts collected by Burning Glass Technologies. The resulting occupational classification comprises four hierarchical layers: the first three layers relate to skill specialisation and group jobs that require similar types of skills. The fourth layer of the hierarchy is based on the offered salary and indicates skill level. The proposed classification will have the potential to enable measurement of an individual's career progression within the same skill domain, to recommend jobs to individuals based on their skills and to mitigate occupational misclassification issues. While we provide initial results and descriptions of occupational groups in the Burning Glass data, we believe that the main contribution of this work is the methodology for grouping jobs into occupations based on skills.

Keywords: labour demand; occupational classification; online job adverts; big data; machine learning; word embeddings (search for similar items in EconPapers)
JEL-codes: C18 J23 J24 (search for similar items in EconPapers)
Date: 2018-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://escoe-website.s3.amazonaws.com/wp-content/ ... SCoE-DP-2018-04U.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nsr:escoed:escoe-dp-2018-04

Access Statistics for this paper

More papers in Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE) King's College London Strand London WC2R 2LS. Contact information at EDIRC.
Bibliographic data for series maintained by ESCoE Centre Manager ().