Publications
Earnings Expectations of "First-in-Family" University Students and Their Role for Major Choice
(with Fabian Kosse, Markus Nagler, and Johannes Rincke)
Abstract: How do students’ earnings expectations differ by being the first in their family to attend university (FiF) and how do they affect field of study choice? We leverage unique survey and administrative data from a German university to document sizable gaps in expected earnings between FiF and non-FiF students. Our data can explain two-thirds of this gap, with the largest share attributable to field of study choice. We show that FiF students sort less into study fields based on their earnings expectations. Investigating potential explanations, we find that they expect lower own ability and worse non-wage amenities in high-earning fields.
IZA Discussion Paper No. 17720
Forthcoming, Labour Economics
Current Papers
Sorting into Career Paths on Personality and Preferences
(with Fabian Kosse, Markus Nagler, and Johannes Rincke)
Abstract: We comprehensively study sorting into career paths on personality and preferences. To do so, we leverage a unique combination of survey and administrative data of several cohorts of students at a large German university. We find extensive sorting along 16 personality and preference measures that persists when controlling for expectations about earnings, own ability, and amenities. This sorting is productive: students whose personality is misaligned with their average peer have a higher likelihood of not completing their degree. Across fields over time, student personality seems rather stable. The sorting into fields of study foreshadows differences in worker personality by occupation.
Draft coming soon!
Gender Discrimination in Online Negotiations - Evidence from a Field Experiment
Abstract: This paper studies gender discrimination in negotiations using a field experiment. To do so, I post job advertisements regarding a home improvement service on an online platform for classified advertisements in Germany. I negotiate offers with all potential sellers of the service who react to the advertisement and randomly vary buyer gender. In the negotiations, two different treatment messages aim at examining statistical discrimination, one with and one without a justification of the negotiation. I find evidence for sellers discriminating against female buyers of the service. Female buyers are less likely than male buyers to receive a counteroffer unless they justify why they negotiate. There are no differences by buyer gender in final offers and price discounts offered by sellers. Analyses of chat data from the platform using large language model (LLM) classifications suggest that sellers engage in statistical discrimination.
Draft coming soon!