Knowledge that Transforms

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.

Prior research has shown that a large fraction of tenured university faculty in the U.S. and other countries were trained at a small number of highly prestigious universities. The question remains whether this concentration is due to competitive advantages held by candidates from these universities, or whether it reflects the larger output of early-career researchers aspiring to faculty positions by these universities. To address this question, we analyze data covering the full population of doctoral graduates in Germany since the 1960s. Similar to studies of the U.S. system of higher education, we observe a strong concentration of professors trained at a small number of universities, with the top five universities accounting for 17.9% of all appointed university professors. However, we find no systematic evidence indicating that the prestige of the doctoral degree-granting university systematically affects individuals' odds of being appointed to professorships, as prestigious universities train disproportionate numbers of doctoral graduates. Despite increasing stratification tendencies within the German system of higher education, our results also do not indicate that the importance of the degree-granting university for the academic careers of its doctoral graduates has increased over the past 50 years. While doctoral graduates from top-tier traditional universities and top-tier technical universities appear to be more likely to secure faculty positions at universities of the same category, this pattern reflects a large share of doctoral graduates returning to their degree-granting university after initial appointments elsewhere. • Tenured faculty concentrate among a small number of PhD-granting universities in Germany • Concentration in faculty appointments tracks concentration in the output of PhDs • No evidence of increasing hiring premium for graduates from highly prestigious universities • Preferential hiring within prestige groups reflects return mobility • Odds of appointment have declined strongly across cohorts since 1960s

We analyze benefits and limitations of information sharing among product market rivals via a smart-contract-enabled blockchain. Firms face uncertain demand but obtain informative signals that can be reported to a blockchain, which ensures immutable recording but does not guarantee truth-telling. Truthful reporting is incentivized by requiring firms to post collateral (“stake”), which is forfeited if the report is deemed untruthful ex-post. We derive (a) conditions under which truthful reporting arises in equilibrium and (b) the necessary amount of collateral that ensures truthful reporting, and examine their comparative statics with respect to ex-ante demand uncertainty, signal precision, and competitive interaction among firms. While the collateral is returned to firms whose production decision is consistent with their report, financially constrained firms may struggle to afford the required stake. Thus, they are potentially unable to join the blockchain. We investigate conditions under which it is beneficial for firms to partially subsidize their rivals’ collateral costs. Finally, we examine welfare implications of information sharing via blockchain, and we show that such information sharing is welfare improving.