Firm training, automation, and wages : international worker-level evidence / Oliver Falck, Yuchen Guo, Christina Langer, Valentin Lindlacher, Simon Wiederhold ; editor: Halle Institute for Economic Research (IWH) - Member of the Leibniz Association
Loading...
Download1.37 MB
Firm training, automation, and wages (revised version, November 7, 2025)
Discovery
1941158323
URN
urn:nbn:de:gbv:3:2-123456789-1179375
DOI
ISBN
ISSN
Koordinaten
Skalierung
dc.contributor.contributor
Other contributor
Publication history
Halle (Saale), Germany : Halle Institute for Economic Research (IWH) - Member of the Leibniz Association, 2025
Content
1 Online-Ressource (III, 42 Seiten, Seite A1-A26, 1,37 MB) : Diagramme
Edition
This version: November 7, 2025
Language
eng
Comment
Literaturverzeichnis: Seite 38-42
Abstract
Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers' automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.
Series
IWH discussion papers ; 2024, no. 27 (December 2024) [rev.] ppn:837399270