Research

How does Artificial Intelligence (AI) affect the task content of jobs, and how do workers adjust to AI’s diffusion? To answer these important questions, we combine novel patent-based measures of AI and robot exposure with individual survey data on job tasks and administrative data on worker careers. Like prior studies, we find that robots have reduced routine tasks. In sharp contrast, AI has reduced abstract tasks like information gathering and increased the demand for complex routine tasks like monitoring processes. These task shifts affect all skill groups, mainly occur within detailed occupations, and become stronger over time. While displacement effects are small, workers have responded by switching jobs, often to less exposed industries. The AI-related task shifts and negative wage effects are stronger for low-skilled workers and older incumbents. Labor market entrants, in turn, face lower entry wages and fewer employment opportunities in exposed industries.

This paper examines preferences for gender diversity among co-workers. Using stated-choice experiments with more than 9,200 professors, PhD students, and university students in Germany, we uncover a substantial willingness to pay (WTP) for gender diversity of up to 5% of earnings on average. Importantly, we find that women have a much higher WTP for gender diversity than men. While the WTP differs by career ambition and related characteristics like competitiveness and family preferences, we find that gender differences in these dimensions cannot explain the gender gap in the WTP for diversity. Our findings provide an explanation for differential sorting of men and women into high-profile jobs based on the share of female co-workers.

We study how the effects of exports on earnings vary across individual workers, depending on a wide range of worker, firm, and job characteristics, and their interactions. To this end, we combine a generalized random forest with an instrumental variable strategy. Analyzing Germany's exports to China and Eastern Europe, we document sharp disparities: workers in the bottom quartile (ranked by the size of the effect) experience little to no earnings gains due to exports, while those in the top quartile see considerable earnings increases. As expected, workers who benefit the most on average are employed in larger firms and have higher skill levels. Importantly, we also find that workers with the largest earnings gains tend to be male, younger, and more specialized in their industry. These factors have received little attention in the previous literature. Finally, we provide evidence that the contribution to overall earnings inequality is smaller than expected.

We use causal machine learning to revisit the link between electronic monitoring (EM) and criminal recidivism. Combining a generalized random forest with the judge instrumental variable design by Di Tella and Schargrodsky (2013), we confirm a negative average effect of EM on rearrest rates. We uncover treatment effect heterogeneity linked to demographics and the criminal history, but the results indicate that no individuals experience an increased rearrest probability from EM. Our findings highlight the benefits of causal machine learning for policy-relevant questions and suggest that the effectiveness of EM depends on the composition of the target population.

Worker perceptions of pay and key job characteristics are important determinants of individual labor market behavior and aggregate labor market outcomes. In this paper, we provide detailed evidence on workers’ valuation of non-wage amenities and their perceptions about the link between wages and amenities across jobs. To this end, we carry out a survey among more than 3,000 workers in Germany. On average, beliefs are in line with the existence of compensating differentials, i.e., a trade-off between wages and amenities. However, perceptions are vastly heterogeneous and differ across amenities and across workers for a given amenity. Workers with recent job search activity, private-sector workers and those with higher education are more likely to believe in compensating differentials.