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...

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Detalles Bibliográficos
Formato: Libro electrónico
Idioma:Inglés
Publicado: Paris : Organization for Economic Cooperation & Development 2023.
Edición:1st ed
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009786727506719
Tabla de Contenidos:
  • Intro
  • Foreword
  • Executive summary
  • 1 Analysis of online job postings in the Umbria region
  • Tracking the evolution of OJPs in Umbria (and Italy) around the COVID-19 crisis
  • Evolution in online job postings by required skill levels
  • What occupations and occupational groups capture the largest share of demand channelled through online job postings in Umbria?
  • A broad view of the labour market demand in Umbria stemming from OJPs
  • High-skill occupations
  • Medium-skill occupations
  • Low-skill occupations
  • Going granular: What specific occupations recorded the largest shares of job postings in Umbria?
  • The evolution of the demand: Which of Umbria's occupations are on the rise?
  • Occupations with emerging demand
  • High-skill occupations with increasing demand in online job postings
  • Medium-skill occupations with increasing demand in online job postings
  • Low-skill occupations with increasing demand in online job postings
  • What are the job characteristics of fast-growing and emerging occupations?
  • Comparison of High, Medium and Low-skilled occupations
  • Demand and supply on the labour market: OJPS versus employment data
  • A discussion of the representativeness of OJPs against LFS data
  • Combining OJPs and LFS data to get insights about labour shortages
  • References
  • Annex 1.A. Job characteristics per skill-level
  • High-skill occupations
  • Medium-skill occupations
  • Low-skill occupations
  • Notes
  • 2 The Regional Training Catalogue and its supply of training: A descriptive analysis
  • What kinds of jobs and skills are the focus of the RTC?
  • The occupations for which training is available in the RTC
  • The courses that are available in the RTC to train for a high-skill profession
  • The courses that are available in the RTC to train for a medium-skill profession.
  • The courses that are available in the RTC to train for a low-skill profession
  • The skills typically offered by the training courses available in the RTC
  • Mapping the skills in the RTC to the skills mentioned in OJPs
  • The cost and length of the training offer in the Regional Training Catalogue
  • Cost of training
  • Duration
  • Class size
  • Differences between Perugia and Terni
  • References
  • Annex 2.A. Selection of results at the province level
  • Notes
  • 3 The alignment between training offered in the Regional Training Catalogue and the labour market
  • Comparing the occupations in the RTC to OJPs
  • Comparing the labour demand and training opportunities across occupations of different skill levels
  • Comparing the alignment between the demand for skills and the skills taught in the RTC
  • Comparing the alignment between the demand for skills and the skills taught in the RTC in courses for different skill-levels
  • The skill-match between labour demand and training supply for each occupation
  • Recent training options: The GOL initiative
  • GOL programme - Reskilling courses
  • Digital skills in the GOL programme
  • References
  • Annex 3.A. Unique skills across skill-levels
  • Notes
  • Annex A. Creating a mapping between the indicators of the demand and supply of skills: Using machine learning to bridge between the RTC and online job postings
  • Step 1: Extracting skills from the RTC
  • Step 2: Creating the semantic representation
  • Step 3: Creating a mapping between keywords in the RTC and the OJPs
  • Step 4: K-means clustering
  • Reference
  • Notes.