Job Market Paper
Bound by Tradition: Cultural Gender Norms and Occupational Choice. (Draft available soon)
Over the last decades, many western economies have experienced a decline in the employment share of traditionally male-dominated occupations, such as manufacturing, whereas traditionally female-dominated occupations show the highest growth rates. Yet, men seem reluctant to enter such occupations. This paper investigates whether cultural gender norms about occupational choice, that is a society's perception of what is appropriate work for men and women, contribute to persistent gender-stereotypical occupational choice. I propose a novel extension of the epidemiological approach and ask whether second-generation immigrant men (women) are less likely to work in an occupation that is perceived as female (male)-typical work in their country of ancestry. I implement the model using both large-scale international survey data and high-quality administrative records. I find robust evidence that men, but not women, adhere to occupation-specific cultural gender norms: men are less likely to work in an occupation that is perceived as female work in their country of origin, while there is no such effect for women.
Presented at: Brown Bag Seminar (Lund), Warwick Economics PhD Conference 2024 (Warwick), International Workshop in Migration and Family Economics 2024 (Paris), ESPE 2024 (Rotterdam), MIT Labor Lunch (Cambridge, MA), Mend the Gap Workshop 2025 (Rome), XVII COSME workshop 2025 (Madrid), URPP Conference on Gender Issues in Education and the Labor Market 2025 (Zurich), FAIR Coffee Meeting (Bergen), Workshop on Future Inequalities: Boys & Men in Focus (Stockholm)*, Ratio Institute (Stockholm)* * = scheduled
Work in Progress
Does AI Help or Hurt Learning? (Draft available upon request)
(with Catalina Franco and Siri Isaksson)
As AI reshapes how students learn, it raises pressing concerns about ensuring equitable learning opportunities and outcomes. A key question is who benefits from AI and who may be left behind. We address this question through a preregistered lab experiment (N=572) examining AI’s impact on learning. Students were randomly assigned to one of three conditions: (1) Control (access to Google Search only), (2) AI-assisted (AI access), or (3) AI-guided (AI access with guidance), and were tasked with learning a novel topic that they had no prior knowledge of. At the end of the experiment, participants completed an exam without AI access, allowing us to causally estimate the effects of AI on learning outcomes. While AI has no overall effect on exam performance, this null masks a significant heterogeneous effect: high-GPA women benefit, while low-GPA men perform worse, raising concerns about whether AI will widen pre-existing educational inequalities. By analyzing participants’ prompt data, we gain insight into the mechanisms behind these effects and identify how different groups interact with AI and how this in turn affects learning outcomes. Our findings reveal that AI may exacerbate learning gaps, and provides key insights for designing policies that can mitigate this risk.
Presented at: ASSA 2025 Annual Meeting in San Francisco (presented by co-author), CEN workshop (Aarhus), 7th Stockholm Uppsala Education Economics Workshop (Uppsala), CESifo / ifo Junior Workshop on the Economics of Education (Munich)
Men Working in Care Jobs.
(with Anne Ardila Brenøe, Louisa Roos, and Daphne Rutnam)
The Long-run Effects of Occupational Experience Programs in Primary School.
(with Petter Lundborg)
Working Papers
(with Jan Bietenbeck and Therese Nilsson)
Conditionally accepted at Economic Inquiry
IZA Discussion Paper 17102, IFN Working Paper No. 1498
(with Jan Bietenbeck, Linn Mattisson, and Felix Weinhardt)
Revise & resubmit ILR Review
CESifo Working Paper 10831, CEP discussion paper 1968
Media coverage: LSE Blog