Using online job vacancies to understand the UK labour market from the bottom-up

Arthur Turrell, James Thurgood (), Jyldyz Djumalieva (), David Copple () and Bradley Speigner ()
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James Thurgood: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Jyldyz Djumalieva: Nesta
David Copple: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH

No 742, Bank of England working papers from Bank of England

Abstract: What type of disaggregation should be used to analyse heterogeneous labour markets? How granular should that disaggregation be? Economic theory does not currently tell us; perhaps data can. Analyses typically split labour markets according to top-down classification schema such as sector or occupation. But these may be slow-moving or inaccurate relative to the structure of the labour market as perceived by firms and workers. Using a dataset of 15 million job adverts posted online between 2008 and 2016, we create an empirically driven, ‘bottom-up’ segmentation of the labour market which cuts across wage, sector, and occupation. Our segmentation is based upon applying machine learning techniques to the demand expressed in the text of job descriptions. This segmentation automatically identifies traditional job roles but also surfaces sub-markets not apparent in current classifications. We show that the segmentation has explanatory power for offered wages. The methodology developed could be deployed to create data-driven taxonomies in conditions of rapidly changing labour markets and demonstrates the potential of unsupervised machine learning in economics.

Keywords: Vacancies; classification; disaggregation (search for similar items in EconPapers)
JEL-codes: C55 J42 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2018-07-27
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur, nep-lma and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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