PIAAC2ESCO - An AI-driven classification of the PIAAC Background questionnaire onto the ESCO Skills Pillar (2021-2022)

DOI

The study concerns the design and use of Artificial Intelligence algorithms for the analysis of information on the labour market. The result is represented by a new mapping of professional skills, which combines the skills detected through the survey on adult skills conducted by the OECD as part of the PIAAC program (International Assessment of Adult Competencies) and the ESCO classification of professional skills in Europe. In particular, PIAAC2ESCO provides a characterisation of the PIAAC background questionnaire on the base of the ESCO Skills Pillar. In practice it associates a list of ESCO skills (v1.0.8) to questions of the PIAAC background questionnaire (version 2010), based on their similarity. The linkage is done using AI in a framework that combines various methods: embeddings, selection of the best embedding, taxonomy alignment and experts' validation. Sections F to I of the PIAAC background questionnaire are used, from which the questions relevant to the analysis (73 out of 84) are extracted  and the best matches with the skills present in the ESCO Skills Pillar (13.600 items) are extracted. The validated dataset covers 21 PIAAC questions and the mapped ESCO skills, which are enriched using alternative labels.

The data is not sample type

other

Identifier
DOI https://doi.org/10.20366/unimib/unidata/SI399-1.0
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=cda9441e95f07c174ffc5ace6668e041832c6293b926a55d117c9ca2b4e3416e
Provenance
Creator Mercorio, Fabio
Publisher UniData - Bicocca Data Archive
Publication Year 2023
Rights UniData metadata records are licensed under a Creative Commons Attribution-Noncommercial 3.0 Italian License; Data are released in according to Creative Commons – Attribution 4.0 Licence, available <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">here</a>.
OpenAccess true
Contact https://www.unidata.unimib.it
Representation
Resource Type Other
Discipline Social Sciences