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.

Choosing a university and field of study is a key life decision that influences one’s lifelong earnings trajectory. Data shows that the share of individuals going to university is unequally distributed, and is lower among disadvantaged students. High-achieving students who are low income are less likely to opt for ambitious education paths, despite the high returns of education. Even among those students who decide to apply for college, the likelihood of whether they will apply to prestigious colleges or renowned study programs differs along the distribution of socioeconomic background. It does not only matter if you study, but also what and where you study, as there is a large variation in long-run outcomes, such as earnings, both between universities as well as between fields of study. Part of this mismatch can be attributed to unequal starting points for children, in terms of both institutional settings and the quality of information available within their close networks.

Kinder mit Migrationshintergrund besuchen im Vergleich zu Kindern ohne Migrationshintergrund deutlich seltener Kindertagesstätten (Kitas), obwohl gerade sie von einem Kitabesuch besonders profitieren würden. Ist sich die Bevölkerung dieser Diskrepanz bewusst? Und wie steht sie zu verschiedenen politischen Maßnahmen, die dieses Problem angehen? Wir untersuchen, ob Informationen über Ungleichheiten und Diskriminierung in der frühkindlichen Betreuung die Zustimmung der Bevölkerung zu gleichheitsfördernden Politikmaßnahmen beeinflussen. Dafür befragen wir 4 800 repräsentativ ausgewählte Personen aus der deutschen Bevölkerung. Es zeigt sich, dass die Befragten oft erhebliche Fehleinschätzungen über das Ausmaß der Ungleichheiten und Diskriminierung beim Kitazugang zwischen Familien mit und ohne Migrationshintergrund haben. Zufällig bereitgestellte Informationen über das tatsächliche Ausmaß dieser Diskrepanzen verringern die politische Polarisierung, d.h. Unterschiede in der Zustimmung zu Unterstützungsmaßnahmen für Familien mit Migrationshintergrund. Unsere Ergebnisse legen nahe, dass verzerrte Wahrnehmungen sozialer Ungleichheiten zu Meinungsunterschieden in der Bevölkerung beitragen. Ein besserer Informationsstand in der Bevölkerung könnte diese Polarisierung in der Zustimmung zu gleichheitsfördernden Politikmaßnahmen beim Zugang zu Kitas verringern.