Publications:
Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms (with Jonas Hannane and Xinrong Zhu) Management Science (2025)
Selected Media Coverage: Wall Street Journal, MIT Sloan Management Review, Washington Post, Fortune, Time, ABC News, NBC News, The Daily Mail, The AP News, Yahoo Finance, Harvard Business School Insights, IB Knowledge, Fast Company, SwissInfo, Vanity Fair, Crisscrossed, Techradar, The Economic Times, Vox Magazine
Awards: Alessandro di Fiore Best Paper Award. Runner-up
How Gen AI Is Already Impacting the Labor Markets? (with Jonas Hannane and Xinrong Zhu) Harvard Business Review (2024)
Working Papers:
In Privacy We Trust: The Effect of Privacy Regulations on Data Sharing Behavior (with Eva Ascarza and Ayelet Israeli)
This paper studies the impact of privacy policies on consumer data-sharing behavior, focusing on policy changes in California and Virginia that took effect in 2023. Using data from a leading customer engagement app in the United States, where users upload shopping receipts in exchange for rewards, we find that privacy regulations led to a significant increase in both the volume and scope of receipt uploads, with the largest increases among users who were initially less inclined to share information. To validate these patterns externally, we analyze the nationally representative Consumer Expenditure Survey metadata, which details respondent interactions during the survey interviews. We find that respondents in treated states became more willing to share spending information after the policy. We further show that states where the new regulation was implemented experienced heightened privacy awareness, evidenced by an increase in privacy-related Google search activity and a decline in expressed privacy concerns during expenditure survey interviews. Together, these findings suggest that privacy regulations may encourage greater consumer participation by improving transparency and trust around data-sharing practices.
Can Gender-Blind Algorithmic Pricing Eliminate the Gender Gap?
Awards: Winner of John A. Howard/AMA Doctoral Dissertation Award, Winner of CESifo Distinguished Affiliate Award; Finalist for American Statistical Association Statistics in Marketing Dissertation Award; Winner of Best Paper Award at Discrimination and Diversity Workshop; Finalist for Routledge Inclusive Economics Award; Winner of Emerging Scholar Award
Insurance companies frequently use consumer attributes, such as gender or age, to set prices for their services. Young male drivers, for example, are often charged more than young females for car insurance, as they are considered as riskier. In 2019, California banned auto insurance companies from using information on gender in their pricing algorithms. I study how this ban affects the gender gap in prices, using a difference-in-differences strategy with older individuals and other states as control groups. I find that the ban reduced the gender gap in the insurance premiums paid by young drivers by around 55 percent, but it failed to eliminate it completely. My analysis of the pricing algorithm of a large insurance company in California indicates that algorithms are adjusted in a way that characteristics that are correlated with the riskiest gender group receive larger weights in pricing after the policy. For instance, drivers using specific car models associated with young males were charged up to 22 percent more after the ban. My findings highlight the limitations of anti-discrimination policies that impose group-blind pricing and provide insights for designing fairer regulations for algorithms.
Reducing Discrimination with Information: Evidence from Online Freelancing Platforms
Hybrid vs. In-person: How Does Online Access to Lectures Affect Student Behavior?
Work in Progress:
Do Online Food Delivery Platforms Help or Harm Restaurant Businesses?