Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/338292 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
CESifo Working Paper No. 11533
Versionsangabe: 
This Version: November 2025
Verlag: 
Munich Society for the Promotion of Economic Research - CESifo GmbH, Munich
Zusammenfassung: 
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.
Schlagwörter: 
job training
human capital
automation
technological change
entropy balancing
JEL: 
J24
J31
J61
O33
Dokumentart: 
Working Paper
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