I am on the job market:
Job Market Paper
+++ Paper conditionally accepted at Quarterly Journal of Economics +++
I am a Ph.D. candidate at the University of Mannheim and a member of the Bonn-Mannheim Collaborative Research Center, funded by the German Research Foundation (DFG). As an Applied Micro-Economist, my research spans Development Economics, Labor Economics, and Political Economy, with a particular interest in mass and social media.
I hold a Master’s degree in Economics from the University of Mannheim and a Bachelor’s degree in Applied Economics from Osnabrück University of Applied Sciences. During my studies, I spent time at Nelson Mandela University in South Africa and Bocconi University, Italy. In 2023, I also completed a research stay at Universitat Pompeou Fabra, Spain.
Job Market Paper
In poor countries, the interaction of early marriage, early motherhood, and low educational attainment disempowers women and limits their life opportunities. Even as countries grow richer, gender inequality is often sustained by social norms, thereby limiting welfare gains from women’s empowerment. I investigate the use of media as a cheap and scalable policy to empower women. In 2006, India enacted a community radio policy that grants radio licenses to NGOs and educational institutions with the aim to foster local development. I collect original data on the content and coverage areas of all 250+ radio stations. I uncover women’s empowerment as a key theme through topic modeling and GPT-based analyses of radio show recordings. For identification, I exploit topography-driven variation in radio access and develop a novel econometric approach to deal with randomly displaced geolocated household data. The results show that women exposed to radio gain an additional 0.3 years of education and are 4.1pp (11%) more likely to obtain a secondary degree. In line with increased education, exposure reduces child marriages by 1.4pp (22%) and fertility of young women by around 10% while they are 11pp more likely to exhibit autonomy in household decisions. The findings demonstrate that community media can effectively address gender inequality.
with Yulia Evsyukova and Wladislaw Mill
Conditionally accepted at Quarterly Journal of Economics
We assess the impact of discrimination on Black individuals’ job networks across the U.S. using a two-stage field experiment with 400+ fictitious LinkedIn profiles. In the first stage, we vary race via A.I.-generated images only and find that Black profiles’ connection requests are 13 percent less likely to be accepted. Based on users’ CVs, we find widespread discrimination across social groups. In the second stage, we exogenously endow Black and White profiles with the same networks and ask connected users for career advice. We find no evidence of direct discrimination in information provision. However, when taking into account differences in the composition and size of networks, Black profiles receive substantially fewer replies. Our findings suggest that gatekeeping is a key driver of Black-White disparities.
Discussion Paper Press Release Latest Thinking: Video Summary AI Generated Podcast
with Antonio Ciccone
with >450 co-authors. The results are in; draft is under preparation.
Abstract of pre-registration: The Multi100 project is a crowdsourced empirical project aiming to estimate how robust published results and conclusions in social and behavioral sciences are to analysts’ analytical choices. Involving more than 200 researchers around the world, we will investigate whether - different analysts arrive at the same conclusions as the author of the original study - different analysts arrive at the same effect estimates as the author of the original study. To answer these questions, 100 empirical studies published in different disciplines of social and behavioral sciences will be re-analyzed by independent researchers. The acceptability of the re-analyses will be judged in a round of peer evaluations. The results of the analyses will be compared in terms of the direction and magnitude of the effect.
New Media & Society, 2022
Politicians have discovered Twitter as a tool for political communication. If information provided by politicians is circulated in ideologically segregated user networks, political polarization may be fostered. Using network information on all 1.78 million unique followers of German Members of Parliament by October 2018, follower homogeneity across politicians and parties is measured. While the overall homogeneity is low, politicians of the AfD —a right-wing populist party —stand out with very homogeneous follower networks. These are largely driven by a small group of strongly committed partisans that make up around 7 percent of the party’s but around 55-75 percent of the average AfD politician’s followers. The findings add to the literature by showing potentially unequal distributions of network segregation on Twitter. Further, they suggest that small groups of active users can multiply their influence online, which has important implications for future research on echo chambers and other online phenomena.
TA: Fall 2021
TA: Spring 2021
in APIs for social scientists: A collaborative review (Editors: Paul C. Bauer, Camille Landesvatter, and Lion Behrens)
A short introduction to API calls to OpenAI’s and Ollama’s Large Language Models using R.