Big Data Intelligence on Skills Demand and Training in Umbria.

Format:

Book

Author/Creator:

Organization for Economic Cooperation & Development, author, issuing body.

Language:

English

Subjects (All):

Labor market--Italy.

Labor market.

Occupational training--Italy.

Occupational training.

Physical Description:

1 online resource (118 pages)

Edition:

1st ed.

Place of Publication:

Paris : Organization for Economic Cooperation & Development, 2023.

Summary:

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.

Contents:

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

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

Annex 2.A. Selection of results at the province level

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

Annex 3.A. Unique skills across skill-levels

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

92-64-50970-4

OCLC:

1439600497