Interests: Industrial Organization, Applied Micro, Experiments, Market Design, Machine Learning, Health Economics, and Agentic AI
This project evaluates affordable housing allocation as a one-sided matching market in which households face significant search and application costs before entering decentralized lotteries. Using a structural model and counterfactual simulations, I compare the status quo uncoordinated waitlists to centralized mechanisms (Deferred Acceptance and Top Trading Cycles) under varying friction regimes. The main result is that participation frictions are the primary driver of inequity: eliminating search and application burdens can remove most justified envy with only modest changes in gross match welfare. In contrast, in highly congested markets like Massachusetts, centralizing the allocation rule alone (e.g., Deferred Acceptance) is close to neutral, while Top Trading Cycles increases welfare but at substantial fairness costs.
This project introduces a practical method for measuring preferences in settings where revealed-preference data are scarce. I construct renter “personas” from the American Community Survey and use a state-of-the-art large language model to evaluate affordable housing opportunities on a 0–10 suitability scale, interpreting these scores as cardinal utilities. The synthetic utilities show strong within-house alignment and meaningful heterogeneity across households, and they respond intuitively to key attributes (rent, school quality, safety, commute time). The approach provides a scalable tool for market design and policy analysis when direct surveys or administrative preference data are unavailable.
Integrated into my JMP
Medicare’s main coverage programs share providers, so policy changes in one program can spill over to another. This paper studies whether incentives in the Medicare Shared Savings Program (MSSP) spill over from Traditional Medicare to Medicare Advantage (MA). Because providers often treat patients across both programs, changes in care delivery under MSSP may affect MA plans beyond the mechanical benchmark formula. To isolate this provider channel, we control for plan-level MA benchmarks and use quasi-experimental variation from the 2019 Pathways to Success reform to construct an instrument for plan exposure to MSSP activity. We find that greater MSSP exposure lowers MA plan bids and raises rebates, suggesting that provider responses to MSSP incentives spill over across Medicare programs and affect outcomes in MA.