Essays in Applied Labor Economics

Chapter 1 -- Citizenship and Integration

Several European countries have reformed their citizenship policies over the past decades. There is much to learn from their experience of how citizenship works; for whom it works; and what rules and policies matter for integration. The article surveys recent quasi-experimental evidence and field experiments from the social sciences on the link between eligibility rules, take-up and integration outcomes. Across countries and reforms, the evidence shows that faster access to citizenship increases take-up and improves the economic, educational, political and social integration of immigrants. Other eligibility rules like civic knowledge tests or application fees also impact who naturalizes and therefore benefits from citizenship. Birthright citizenship, which is much less common in Europe, turns out to be a powerful tool for getting second-generation immigrants off to a good start. Together, citizenship acts as a powerful catalyst benefiting immigrants as well as host countries.

Chapter 2 -- Arriving LATE: Access to Citizenship and Economic Integration

We analyze whether faster access to citizenship fosters the economic integration of immigrants. Our empirical setting is Germany, which went from a strict concept of citizenship based on `jus sanguinis' to a more open citizenship policy. We make use of discontinuities in residency requirements faced by first-generation immigrants to estimate LATEs based on Local Randomization and Fuzzy RDD approaches. We find that a more liberal citizenship policy acts as a catalyst for integration, especially for immigrant women. Women's labor force participation increases by 8.9 percentage points and their earnings by 21.3%. We do not find any significant effects on immigrant men.

Chapter 3 -- Small Pictures, Big Biases: The Adverse Effect of an Airbnb Design Intervention

A 2018 Airbnb design intervention reduced the size of the host profile picture, creating a natural experiment to test whether the salience of visual cues affects racial bias in the demand for Airbnb listings. Using scraped data from Airbnb in New York City and a face classification model, we find that, unexpectedly, the new design increased the Black-White demand disparity by 3.3 percentage points, an increase of about 30% relative to the pre-intervention gap. We show that smaller images made it harder for guests to detect positive facial cues -- especially smiles -- that are typically associated with higher demand, leading them to rely more heavily on skin color. In response, Black hosts updated their profile pictures to make their faces more visible and added basic amenities to their listings.

Chapter 4 -- Mapping Artificial Intelligence: Evidence from Firm-Level Web Data in Europe

I develop a novel firm-level indicator of Artificial Intelligence (AI) adoption in Europe by applying a Large Language Model to more than four million firm websites from Belgium, France, Germany, and Luxembourg (2016–2024). The method detects not only whether firms adopt AI, but also their role in the AI ecosystem and the type of technology they employ. The share of AI-active firms grew from 1% in 2016 to almost 12% in 2024, with acceleration after 2022. I document a structural transformation: the ecosystem is shifting from specialized AI core technology providers toward broader AI adopters and application developers, signaling widespread diffusion. While adoption is concentrated among larger, younger, knowledge-intensive firms in urban innovation clusters, workforce skills emerge as a key factor for AI adoption. The results suggest that foundational Data skills form the necessary base for adoption, while specialized AI skills act as strong complements. My website-based measure diverges meaningfully from industry exposure-based indices, revealing that actual adoption often differs from potential exposure.