What is the price of a skill? The value of complementarity
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The global workforce is urged to constantly reskill, as technological change favours particular new skills while making others redundant. But which skills are a good investment for workers and firms? As skills are seldomly applied in isolation, we propose that complementarity strongly determines a skill's economic value. For 962 skills, we demonstrate that their value is determined by complementarity – that is, how many different skills, ideally of high value, a competency can be combined with. We show that the value of a skill is relative, as it depends on the skill background of the worker. For most skills, their value is highest when used in combination with skills of a different type. We put our model to the test with a set of skills related to Artificial Intelligence (AI). We find that AI skills are particularly valuable – increasing worker wages by 21 % on average – because of their strong complementarities and their rising demand in recent years. The model and metrics of our work can inform the policy and practice of digital re-skilling to reduce labour market mismatches. In cooperation with data and education providers, researchers and policy makers should consider using this blueprint to provide learners with personalised skill recommendations that complement their existing capacities and fit their occupational background.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 104898 |
| Tidsskrift | Research Policy |
| Vol/bind | 53 |
| Udgave nummer | 1 |
| ISSN | 0048-7333 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
The authors are very thankful to Estrella Gómez Herrera for an extensive collegial review of the work. Similarly, we thank Ingo Zettler and Magnus Lindgaard Nielsen for their feedback on a presentation of the paper and to Vili Lehdonvirta, Hendrik Send, and Georg von Richthofen for initial brainstorming on the work. The authors are grateful for the wonderful copy editing work by David Sutcliffe. The authors furthermore are grateful to Fabian Braesemann for curation of the underlying data, which has been retrieved under the John Fell Oxford University Press Research Fund, grant number 0008391. Fabian Stephany thanks the ESRC Digit Innovation Fund (G2781-15) and the programme on AI & Work funded by the Dieter Schwarz Foundation gGmbH for the financial support of his work.
Funding Information:
The authors are very thankful to Estrella Gómez Herrera for an extensive collegial review of the work. Similarly, we thank Ingo Zettler and Magnus Lindgaard Nielsen for their feedback on a presentation of the paper and to Vili Lehdonvirta, Hendrik Send, and Georg von Richthofen for initial brainstorming on the work. The authors are grateful for the wonderful copy editing work by David Sutcliffe. The authors furthermore are grateful to Fabian Braesemann for curation of the underlying data, which has been retrieved under the John Fell Oxford University Press Research Fund , grant number 0008391 . Fabian Stephany thanks the ESRC Digit Innovation Fund ( G2781-15 ) and the programme on AI & Work funded by the Dieter Schwarz Foundation gGmbH for the financial support of his work.
Publisher Copyright:
© 2023 The Authors
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