We build a granular dataset of residential property yields using rental and sale listings from a major German real estate platform. With more than 1.5 million property-level rent-to-price ratios, we document a novel heterogeneity puzzle. About two-fifths of yield dispersion remains unexplained after controlling for extensive observable property characteristics and postal code-level time-varying factors using fixed effects. When we absorb finer neighborhood amenities through richer fixed effects, the unexplained share remains close to one-third. This evidence points to the importance of investors’ beliefs and preferences—rather than several alternative explanations we test—as sources of heterogeneity in risk premia.

We focus on listed non-financial companies from the largest European economies to explore the impact of rising stakeholders’ attention towards climate risk on firms’ probability to delist. An aggregate analysis shows that, despite growing awareness about the climate problem, enhanced scrutiny over more polluting firms and improved access to information about corporate sustainability over the last two decades, the share of carbon-intensive industries has not significantly declined in the listed sector relative to the whole economy. Also at the micro-level, firms in carbon-intensive sectors have not become more likely to delist, even in periods of enhanced stakeholders’ concern about climate risk, potentially because they experienced a shift towards lower emissions over the same period. We confirm this result by means of a quasi-natural experiment centered around the enhanced disclosure requirements introduced by the EU Non-Financial Reporting Directive of 2014.

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.