Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308050.2 
Year of Publication: 
2025
Series/Report no.: 
IWH Discussion Papers No. 27/2024
Version Description: 
This version: November 7, 2025
Publisher: 
Halle Institute for Economic Research (IWH), Halle (Saale)
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.
Subjects: 
automation
entropy balancing
firm training
human capital
on-the-job training
technological change
JEL: 
J24
J31
J61
O33
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:




Version History
Version Item Summary
2 10419/308050.2 This version: November 7, 2025
1 10419/308050 First version: December 17, 2024

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.