Both the demand for skilled labor and the skill wage premium have become increasingly dispersed across U.S. regions. This paper studies how technological change within occupations drives these uneven regional developments. Using a new measure capturing shifts in task intensities within 430 detailed occupations, the analysis shows that innovation reallocates labor toward cognitive-intensive tasks, especially in densely populated labor markets. Greater exposure to such task reallocation increases relative employment growth for college-educated workers while reducing wages for less-educated workers, thereby widening local wage gaps between skill groups. About 80 percent of the estimated wage effects are due to within-occupation adjustments.

Although more than 20 per cent of the workforce changes their occupation every year, we still do not fully understand the mechanisms behind the observed mobility. This paper focuses on analysing the relationship between work-hour instability and occupational mobility in the US labour market. I use the longitudinal dimension of the Current Population Survey (CPS) to measure individuals’ intra-year work-hour variation and analyse their mobility through a balanced occupation panel. Being in the highest quartile of work-hour variation is associated with a higher mobility rate of 0.33% for men and 0.81% for women compared to an average monthly mobility rate of 1.71%. Analysing the predicted marginal effects across different household compositions suggests that the substantial gender gap can be explained by the intra-household specialisation of men and women. The last part of this study shows that only workers with highly volatile work hours sort themselves into more stable occupations.

This project investigates how AI-related innovation reshapes the task content of occupations compared to previous technological waves. Building on the theoretical framework of Acemoglu and Restrepo (2019), we develop two measures of technological change: one capturing task automation and another capturing new task creation. We use large language models (LLMs) to link patent abstracts to a multidimensional task and tools space covering 430 occupations. To distinguish AI from non-AI innovation, we embed the full universe of U.S. patent texts (1980–2015) and compute their cosine similarity to the AI taxonomy developed by Giczy et al. (2022). Applied to local labor markets in the U.S., our empirical analysis reveals two key distinctions in how AI-driven technological change differs from previous waves: (1) AI innovation is more prevalent in regions with low routine task intensity; and (2) AI-related technologies substantially increase the relative demand for cognitive skills only for high-skilled workers. These findings highlight a fundamental shift in how technology reshapes skill requirements and the future of work.

Recent task-based research by Autor and Thompson (2025) highlights that employment and wages within occupations may evolve in opposite directions, depending on how technological change alters the expertise content of jobs. Building on this insight, our project studies how these mechanisms operate in European labor markets. We extend the expertise-based approach by introducing a local dimension, recognizing that the same occupation may involve different tasks and skill requirements across regions—even within the same country. Using this framework, we investigate how uneven adjustments in job expertise contribute to divergent wage and employment dynamics across space.

In this project, we leverage unique Polish registry data on ex-prisoners and exploit a policy reform that increased the minimum wage of prisoners to the same level as the statutory minimum wage. After a statewide implementation of this policy, it became more difficult for incarcerated individuals to find a job, which is essential for them to collect work days that qualify for unemployment benefits upon release. This setting creates a compelling quasi-experiment to study the effect of liquidity on re-employment outcomes for ex-prisoners. For causal identification, we use the prison entry date, which determines exposure to the high-cost prison work regime, as an instrument for qualifying for unemployment insurance. Naïve OLS estimates suggest that benefit claimants reintegrate into the labor market more quickly compared to non-claimants. In contrast, using the IV identification strategy reveals that claimants fare worse than non-claimants, remaining unemployed for longer. These findings underscore the importance of accounting for selection into prison work and suggest that a within-prison work policy that eases liquidity after release may unintentionally discourage job search.

Women are significantly underrepresented among innovation leaders, including startup founders, patent inventors, and venture capitalists. This raises the question of whether technological change is gender-biased in ways that systematically favor masculine work values and male-dominated occupations, and how such dynamics shape the evolution of the female labor market. I investigate these questions for the U.S. by constructing a measure of gender-biased technological change based on time-varying occupation data combined with female employment shares across occupations, and by linking this measure to patent data, decennial Census data, and political survey data. The pre-analysis shows that recent technological change is associated with declining demand for skills and work values traditionally emphasized in female-dominated occupations, alongside relative demand shifts toward male-dominated, cognitive-intensive jobs. These patterns suggest polarizing effects within the female labor market, with college-educated women adjusting more flexibly to technology-driven changes than women without a college degree, widening gaps in job content, family orientation, and political ideology between low- and high-skilled women.

The recent evolution of employment indicates that the growth in the demand for cognitive ability in the U.S. labour market has flattened since the 2000s. This paper sheds light on this puzzle using updated O*NET ability data to measure cognitive and manual task intensity changes within occupations between 2008 and 2017. Non-routine cognitive and high-wage occupations increased while routine-intensive and low-wage occupations decreased in cognitive intensity. At the same time, the decline in manual intensity is more substantial in non-routine-cognitive and high-wage occupations. The differential task demand changes between occupations imply heterogeneous effects on the segmented labour market. Young men, workers with college degrees, and workers in STEM occupations experienced the most substantial increase in the demand for cognitive ability. The effects of task changes within occupations account for an 8.3% increase in the return to cognitive task intensity between 2008 and 2017.