I am a Doctoral candidate at the University of Bremen. My primary research fields are behavioral economics and industrial organization. I am specifically interested in exploring the economic implications of digital transformation, with a focus on competition, welfare, and regulation. Methodologically, I usually work with observational data and conduct online, laboratory, and field experiments.
In January 2024, I will join the Digital Economy Department at ZEW as a Postdoctoral Researcher.
You can download my CV here.
PhD in Economics (expected 2024)
University of Bremen
MSc in Economics
BSc in Social Sciences
This paper investigates how workers' skills and job application behavior contribute to the gender wage gap using data from a leading online labor platform. We utilize machine learning models to quantify the value of workers' skills and estimate their impact on wages. We find a substantial raw gender wage gap of over 30% that can, however, be fully accounted for by three factors: differences in workers' skills, differences in the projects they apply to, and differences in asking wages. Our findings indicate no employer discrimination based on gender. Instead, the gender wage gap emerges because men and women seem to use the platform in different ways. Women prioritize consistent income, while men pursue higher-paying, occasional gigs. These differences likely stem from different constraints and labor market opportunities outside the platform. According to our results, the flexibility of the online gig economy is unlikely to favor women.
Online platforms that implement reputation mechanisms usually prevent the transfer of ratings to other platforms, leading to lock-in effects and high switching costs for users. This situation can be capitalized by platforms, for example, by charging their users higher fees. In this paper, we theoretically and experimentally investigate the effects of platform pricing on workers' switching behavior in online labor markets and analyze whether a policy regime with reputation portability could mitigate lock-in effects and reduce the likelihood of worker capitalization by the platform. We further examine switching motives more thoroughly and differentiate between monetary motives and fairness preferences. Theoretically, we provide evidence for the existence of switching costs if reputation mechanisms are platform-specific. The model predicts that reputation portability lowers switching costs, eliminating the possibility for platforms to capitalize lock-in effects. We test our predictions using an online lab-in-the-field experiment. The results are in line with our theoretical model and show that the absence of reputation portability leads to worker lock-in, which can be capitalized by platforms. Moreover, reputation portability has a positive impact on the wages of highly rated workers. The data further show that the switching of workers is primarily driven by monetary motives, but perceiving the platform fee as unfair also plays a significant role for workers.