- JRC nr: JRC125917
- 30 elokuu 2021
Data from online job advertisements are increasingly used in the emerging area of “skills intelligence” to describe labour market dynamics and the demand for skills in different occupations. Collecting this data involves gathering unstructured information from the internet and processing it into structured datasets, which may provide a biased description of the labour market. We present a framework for these different sources of bias, in terms of representativeness of occupations and their task content. We analyse the Nova UK dataset of online job advertisements from Burning Glass Technologies, containing over 60m individual job ads for the United Kingdom from 2012–2020. We compare the occupation task profiles embedded in this data with the JRC-Eurofound Task Database, through a new Skill-Task Dictionary. The dictionary classifies the rich but unstructured information on “skills” describing individual occupations into the hierarchical Task Taxonomy developed by the JRC and Eurofound, and measured through occupation surveys. In general, we find that the task profile implied in job advertisements is relatively consistent with the EU Task Database across most occupations, especially for intellectual and social tasks, and for tools of work. However, online job advertisements in general (and Nova UK in particular) tend to focus especially on professional occupations, which are relatively better represented in their numbers and in their variety of skills and tasks, relative to less qualified occupations. We enumerate several types of bias that can occur with this data, and discuss possible future applications.
SOSTERO Matteo, FERNANDEZ MACIAS Enrique