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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.
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Keywords: skills, PIAAC, ESCO, AI, embedding
Geographical Unit: not applicable
Analysis Unit: other
Universe: No reference universe
Sample Procedure: The data is not sample type
Weight: No weight used
Collection Mode: other
Collection Size: UniData supplies: 1 dataset in SPSS format, 1 dataset in CSV format, 1 methodological notes in PDF format (eng), 1 codebook in PDF format (eng) (4 file)
|Methodological Notes (pdf):||Codebook (pdf):|
- Guo, Y., Langer, C., Mercorio, F., Trentini F. (2022) Skills Mismatch, Automation, and Training: Evidence from 17 European Countries Using Survey Data and Online Job Ads. EconPol Forum 23 (5), 11-15. CESifo, Munich, 2022
Data Use Restriction:
Data are released in according to Creative Commons – Attribution 4.0 Licence, available here.
Source Contact: Francesco Trentini - Università degli Studi di Milano-Bicocca
Mercorio, Fabio. (2021-2022) PIAAC2ESCO – An AI-driven classification of the PIAAC Background questionnaire onto the ESCO Skills Pillar. Mezzanzanica, Mario [Producer]. Pallucchini, Filippo [Producer]. Trentini, Francesco [Producer]. Guo, Yuchen [Producer]. Langer, Christina [Producer]. UniData - Bicocca Data Archive, Milan. Study Number SI399. Data file version 1.0 doi:10.20366/unimib/unidata/SI399-1.0
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