Firm Training, Automation, and Wages: International Worker-Level Evidence
Oliver Falck, Yuchen Guo, Christina Langer, Valentin Lindlacher, Simon Wiederhold
Research Policy,
Vol. 55 (3),
2026
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.
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Firm Training, Automation, and Wages: International Worker-Level Evidence
Oliver Falck, Yuchen Guo, Christina Langer, Valentin Lindlacher, Simon Wiederhold
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.
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Filippo Biondi, Sergio Inferrera, Matthias Mertens, Javier Miranda
VoxEU CEPR,
2024
Abstract
We study changes in business dynamism in Europe after 2000 using novel micro-aggregated data that we collected for 19 European countries. In all countries, we document a broad-based decline in job reallocation rates that concerns most economic sectors and size classes. This decline is mainly driven by dynamics within sectors, size, and age classes rather than by compositional changes. Large and mature firms experience the strongest decline in job reallocation rates. Simultaneously, the employment shares of young firms decline. Consistent with US evidence, firms’ employment has become less responsive to productivity shocks. However, the dispersion of firms’ productivity shocks has decreased too. To enhance our understanding of these patterns, we derive and apply a novel firm-level framework that relates changes in firms’ sales, market power, wages, and production technology to firms’ responsiveness and job reallocation.
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