Big Data Intelligence on Skills Demand and Training in Umbria
The COVID-19 pandemic had a severe impact on the Umbrian economy, and despite recovery of labour demand, the region faces challenges related to digitalisation, tight labour markets, and volatile demand for low-skilled jobs. To address these issues, the OECD and the Umbrian regional agency for active labour market policies (ARPAL) have collaborated to investigate the labour and skills demand of the region using big data techniques applied to online job postings. This report provides new insights into the alignment between labour and skills demand and the training options available in the training and education programmes contained in the Umbrian Regional Training Catalogue. This report builds new indicators to measure the alignment of course content with employer demands in Umbria, with results showing that alignment is relatively good for some occupations but that this can be strengthened to provide job seekers with up-to-date training options that match the demand of the labour market.
The alignment between training offered in the Regional Training Catalogue and the labour market
This chapter examines the alignment between the courses listed in the Regional Training Catalogue (RTC) and the demands reflected in online job postings, considering both sought-after occupations and skills. Utilising Natural Language Processing techniques, the chapter analyses the quantitative and qualitative match between the course content and the skill demand for each occupation included in the RTC. A novel metric, the skill-match score, is introduced by integrating data on sought-after skills from online job postings and the representation of these skills in the courses. Additionally, the chapter offers insights into potential areas where training may not yet adequately meet demand or exceed it within the analysed occupations and skill sets. These findings serve as preliminary indicators for policymakers, aiding in interventions to enhance training offerings or allocate resources accordingly.
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