Developing experimental estimates of regional skill demand

Stef Garasto (), Jyldyz Djumalieva (), Karlis Kanders, Rachel Wilcock and Cath Sleeman ()

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

Abstract: This paper shows how novel data, in the form of online job adverts, can be used to enrich social labour market statistics. We use millions of job adverts to provide granular estimates of the vacancy stock broken down by location, occupation and skill category. To derive these estimates, we build on previous work and deploy methodologies for a) converting the flow of job adverts into a stock and b) adjusting this stock to ensure it is representative of the underlying economy. Our results benefit from the use of duration data at the level of individual vacancies. We also introduce a new iteration of Nesta’s skills taxonomy. This is the first iteration to blend an expert-derived collection of skills with the skills extracted from job adverts. These methodological advances allow us to analyse which skill sets are sought by employers, how these vary across Travel To Work Areas in the UK and how skill demand evolves over time. For example, we find that there is considerable geographical variability in skill demand, with the stock varying more than five-fold across locations. At the same time, most of the demand is concentrated among three categories: "Business, law and finance", "Science, manufacturing and engineering" and "Digital". Together, these account for more than 60 per cent of all skills demanded. The type of intelligence presented in this report could be used to support both local and national decision makers in responding to recent labour market disruptions.

Keywords: big data; labour demand; machine learning; online job adverts; skills; word embeddings (search for similar items in EconPapers)
JEL-codes: C18 J23 J24 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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