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AI meets labor market: Exploring the link between automation and skills

Author

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  • Colombo, Emilio
  • Mercorio, Fabio
  • Mezzanzanica, Mario

Abstract

This paper develops a set of innovative tools for labor market intelligence by applying machine learning techniques to web vacancies on the Italian labor market. Our approach allows to calculate, for each occupation, the different types of skills required by the market alongside a set of relevant variables such as region, sector, education and level of experience. We construct a taxonomy for skills and map it into the recently developed ESCO classification system. We subsequently develop measures of the relevance of soft and hard skills and we analyze their detailed composition. We apply the dataset constructed to the debate on computerization of work. We show that soft and digital skills are related to the probability of automation of a given occupation and we shed some light on the complementarity/substitutability of hard and soft skills.

Suggested Citation

  • Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.
  • Handle: RePEc:eee:iepoli:v:47:y:2019:i:c:p:27-37
    DOI: 10.1016/j.infoecopol.2019.05.003
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    Cited by:

    1. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    3. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.
    4. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    5. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.
    6. Emilio Colombo & Alberto Marcato, 2021. "Skill Demand and Labour Market Concentration: Theory and Evidence from Italian Vacancies," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis2104, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).

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    More about this item

    Keywords

    Machinelearning; Web vacancies; Skill analysis; Automation;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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