Using scraped data from Airbnb's platform in New York City alongside state-of-the-art Vision Transformer models for image classification, this paper investigates the existence and extent of race discrimination in the Airbnb platform and the impact of a policy to reduce this bias. First, we show that Black hosts have a 7.2 percentage points lower occupancy rate than their White counterparts despite no differences in pricing. For Asian and Hispanic hosts, the difference compared to White hosts is small and largely insignificant for both occupancy rates and prices. Second, using difference-in-differences and event-study approaches, we show that the 2018 Airbnb anti-discrimination policy, which reduced the size of users' profile pictures on the platform, unexpectedly increased the Black-White disparity by about 3.3 percentage points. As a reaction to the adverse impact of the new policy, Black hosts increased the number of amenities in their listings. A potential mechanism for the increase in Black-White disparity stems from the increasing guests' uncertainty in discerning facial features that positively correlate with occupancy rates from the smaller profile pictures. As a result, guests focus more on skin color.

This paper tests how academic affiliation affects conference acceptance. Using a field experiment in an economics workshop, we find that disclosing affiliation biases reviewers in favor of authors from prestigious institutions. This bias, driven by in-group favoritism, suggests that affiliation bias may reinforce inequalities and limit academic diversity.

This study examines the role of ESG indicators in predicting financial distress in 362 US and EU-28 commercial banks (2012-2019). It finds that ESG factors improve the accuracy of distress prediction and reduce misclassification of troubled banks. The model, using statistical, machine learning, and ensemble methods, offers practical insights for banking supervisors and contributes to default prediction research.