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.

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 investigate public preferences for equity-enhancing policies in access to early child care, using a survey experiment with a representative sample of the German population (n ≈ 4, 800). We observe strong misperceptions about migrant-native inequalities in early child care that vary by respondents’ age and right-wing voting preferences. Randomly providing information about the actual extent of inequalities has a nuanced impact on the support for equity-enhancing policy reforms: it increases support for respondents who initially underestimated these inequalities, and tends to decrease support for those who initially overestimated them. This asymmetric effect leads to a more consensual policy view, substantially decreasing the polarization in policy support between under- and overestimators. Our results suggest that correcting misperceptions can align public policy preferences, potentially leading to less polarized debates about how to address inequalities and discrimination.

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.

We investigate public preferences for equity-enhancing policies in access to early child care, using a survey experiment with a representative sample of the German population (n ≈ 4, 800). We observe strong misperceptions about migrant-native inequalities in early child care that vary by respondents’ age and right-wing voting preferences. Randomly providing information about the actual extent of inequalities has a nuanced impact on the support for equity-enhancing policy reforms: it increases support for respondents who initially underestimated these inequalities, and tends to decrease support for those who initially overestimated them. This asymmetric effect leads to a more consensual policy view, substantially decreasing the polarization in policy support between under- and overestimators. Our results suggest that correcting misperceptions can align public policy preferences, potentially leading to less polarized debates about how to address inequalities and discrimination.