Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - April 2022)
The past decade has witnessed the emergence of online platform markets in various industries. One defining characteristic of platform markets is that it directly connects individual sellers and buyers, generating substantial benefits for both sides. This dissertation investigates what drives buyers' and sellers' participation in platform markets and what benefits they obtain from the participation.The first essay (in Chapter 2) investigates the impact of pro-social and monetary incentives on doctors' price and quality on a health consultation platform and quantifies patients' accessibility and welfare benefits. I develop a structural demand and supply model that captures the key characteristics of the online consultation market. Using a detailed consultation level dataset from China, I estimate patients' preference for consultation price and quality as well as doctors' cost and pro-social preference for providing consultation services. Through counterfactual analysis, I find that, as compared to the existing policy in which doctors set prices individually, an alternative two-point pricing policy can improve patients' accessibility, surplus, and doctors' surplus.The second essay (in Chapter 3) further examines doctors' pro-social incentives in the health consultation platform by leveraging a volunteering program during the COVID-19 pandemic. The Chinese government initiated a volunteering program that recruited volunteer doctors and nurses to treat COVID-19 patients. I find a divergent impact of volunteering on volunteer doctors and their coworkers. The results suggest that volunteering hurts doctors' consultation quantity and quality but has a positive impact on coworkers' consultation quantity and quality. I argue that volunteering leads to time constraints and pro-social incentive decline for volunteer doctors but pro-social incentive increase for their coworkers.The third essay (in Chapter 4) studies the benefits for sellers' participation in the sharing economy platform by empirically quantifying the impact of Airbnb listings on housing foreclosure. To overcome the data limitation due to the platform privacy protection, I develop a novel probabilistic model to estimate the Airbnb impact on individual property's foreclosure risk while explicitly accounting for the self-selection issue. The results show that being listed on Airbnb can substantially reduce homeowners' foreclosure risk, especially for more vulnerable homeowners.