Skills requirements across task-content groups in Poland: What online job offers tell us
Lukasz Arendt,
Ewa Gałecka-Burdziak,
Fernando Núñez,
Robert Pater and
Carlos Usabiaga
Technological Forecasting and Social Change, 2023, vol. 187, issue C
Abstract:
Dynamic digitalisation has a profound impact on labour markets, leading to routinisation and job polarisation processes. Most studies on labour polarisation use employment data to track changes in labour demand and incomes across routine and non-routine jobs. In this paper we utilise a database of online job postings published on Polish websites in the period 2017–2019 to map skills the requirements in respective task-content groups. We observe little evidence of job polarisation in the Polish vacancy market, with cognitive, communication, availability, technical, and self-organisation skills being in high demand regardless of the task-content group. With the use of logistic regression we find that non-routine and routine cognitive jobs require a quite extensive, and similar skill-mix. Multinomial logistic results show that communication and cognitive skills enhance the employability in non-routine and routine cognitive jobs, while in the case of non-routine manual jobs these are availability, mathematical and interpersonal skills. Based on these findings, we conclude that the lifelong learning system should focus on developing the transversal skills required in jobs resilient to automation (non-routine jobs). Moreover, we argue that enhancing the transition from routine to non-routine jobs by providing the required transversal skills plays and important role in reducing social inequalities.
Keywords: Online job offers; Routinisation; Job polarisation; Skills requirements; Logistic regression (search for similar items in EconPapers)
JEL-codes: C38 C55 J24 O33 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007661
DOI: 10.1016/j.techfore.2022.122245
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