2026-05-27T02:46:58Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

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XML DDI-C 2.5 Documentation for study SI399-1.0 UniData - Bicocca Data Archive UniData metadata records are licensed under a Creative Commons Attribution-Noncommercial 3.0 Italian License PIAAC2ESCO - An AI-driven classification of the PIAAC Background questionnaire onto the ESCO Skills Pillar (2021-2022) PIAAC2ESCO - Una classificazione AI-driven del questionario PIAAC su ESCO Skills Pillar (2021-2022) SI39910.20366/unimib/unidata/SI399-1.0 Mercorio, Fabio 2023-02-03 UniData - Bicocca Data Archive UniData - Bicocca Data Archive skillsPIAACESCOAIembedding LABOUR AND EMPLOYMENT - employmentLABOUR AND EMPLOYMENT - working conditionsSCIENCE AND TECHNOLOGY - information technology 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. other No reference universe other other The data is not sample type other 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>. 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 The user is obliged to quote all data and documents disseminated by UniData and used in the own publications, using the information previously showed. The user is also obliged to send UniData the bibliographic citations related to the publications where the requested data and documents are used Methodological Notes 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 http://oai.unidata.unimib.it/v0/oai SI399 2023-02-09T08:31:01Z ddi:codebook:2_5