Job 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 job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.

We analyse the impact of robot adoption on employment composition using novel micro data on robot use in German manufacturing plants linked with social security records and data on job tasks. Our task-based model predicts more favourable employment effects for the least routine-task intensive occupations and for young workers, with the latter being better at adapting to change. An event-study analysis of robot adoption confirms both predictions. We do not find adverse employment effects for any occupational or age group, but churning among low-skilled workers rises sharply. We conclude that the displacement effect of robots is occupation biased but age neutral, whereas the reinstatement effect is age biased and benefits young workers most.

Does increasing common ownership influence firms’ automation strategies? We develop and empirically test a theory indicating that institutional investors’ common ownership drives firms that employ workers in the same local labor markets to boost automation-related innovation. First, we present a model integrating task-based production and common ownership, demonstrating that greater ownership overlap drives firms to internalize the impact of their automation decisions on the wage bills of local labor market competitors, leading to more automation and reduced employment. Second, we empirically validate the model’s predictions. Based on patent texts, the geographic distribution of firms’ labor forces at the establishment level, and exogenous increases in common ownership due to institutional investor mergers, we analyze the effects of rising common ownership on automation innovation within and across labor markets. Our findings reveal that firms experiencing a positive shock to common ownership with labor market rivals exhibit increased automation and decreased employment growth. Conversely, similar ownership shocks do not affect automation innovation if firms do not share local labor markets.

Using a newly collected dataset at the plant level from 2014 to 2018, we provide the first microscopic portrait of robotization in Germany and study the correlates of robot adoption. Our descriptive analysis uncovers five stylized facts: (1) Robot use is relatively rare. (2) The distribution of robots is highly skewed. (3) New robot adopters contribute substantially to the recent robotization. (4) Robot users are exceptional. (5) Heterogeneity in robot types matters. Our regression results further suggest plant size, high-skilled labor share, exporter status, and labor shortage to be strongly associated with the future probability of robot adoption.