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I am an empirical economist working at the intersection of market design, industrial organization, and public economics. My research investigates how to better design public housing systems, platform marketplaces, and transportation policy.

I am a Presidential Fellow at the Department of Strategy and Policy at the National University of Singapore (NUS) Business School. In Summer 2025, I will be appointed an Assistant Professor at NUS.

I spent a postdoctoral stint at the Cowles Foundation at Yale University. I obtained my PhD from Princeton University after formative years at the University of Chicago and Washington University in St. Louis.


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Working Papers

(Click on paper descriptions to see abstracts)


Public housing at scale

We consider the design of a large-scale public housing program where consumers face dynamic tradeoffs over apartments rationed via lotteries and prices. We show, theoretically and empirically, that changing rules complements increasing supply. First, we present a motivating example in which supplying more housing leads households to strategically delay their applications. By waiting for “better” developments arriving tomorrow, households forgo mediocre developments available today, resulting in more vacancies. Turning to the data from the mechanism, we formulate a dynamic choice model over housing lotteries and estimate it. Under the existing mechanism, we find that increasing supply fails to lower wait times. However, when a strategyproof mechanism is implemented, vacancies and wait times fall, but prices on the secondary market rise. Under this new mechanism, building more apartments lowers wait times and reduces the upward pricing pressure on the secondary market.

Entry into two-sided markets shaped by platform-guided search
  • Joint work with Leon Musolff.
  • Awarded the Rising Star Paper Prize at the 20th International Industrial Organization Conference (2022)
  • New version (Sep 2023)! Submitted.

Consider firms that operate platforms matching buyers and sellers while selling goods themselves. By guiding consumers towards their own products through algorithmic recommendations, these firms could influence market outcomes - a regulatory concern. To investigate, we combine novel data about sales and recommendations on Amazon with a structural model that captures seller entry. Recommendations are highly price-elastic (-20), and many consumers (34%) only consider recommended offers. Hence, algorithmic recommendations raise the demand elasticity (from -8 to -11), intensify price competition, and increase the purchase rate. However, increased competition reduces entry (but the missing merchants are the least efficient). Focusing on self-preferencing: recommendations favor Amazon (equivalent to a 6% price discount), but this skew does not act as a barrier to entry or otherwise harm consumers. Indeed, since consumers prefer Amazon’s offers, “self-preferencing” slightly raises consumer surplus by $9 per product per month (assuming Amazon’s prices remain constant.)

Urban transit infrastructure and inequality

We propose a quantitative spatial model featuring heterogeneous worker groups and travel to consume non-tradable goods and services. Using transit farecard data from Singapore, we establish that low-income workers are more sensitive to changes in travel time than high-income workers. Since the former are overwhelmingly employed in non-tradable sectors, changes in consumption travel can induce a spatial re-organization of low-income jobs in the city. The Downtown Line resulted in large welfare gains for high-income workers, but near zero for low-income workers. All workers enjoyed improved access to consumption opportunities, but low-income jobs in the non-tradable sector moved to less attractive workplaces. Abstracting from consumption travel understates the disparate impact of the DTL across worker groups three-fold.

Build to order: Endogenous supply in centralized mechanisms

How should the supply of public housing be optimally designed? Although commonly used queuing mechanisms treat the supply of goods as exogenous, designers can often control the inflow of goods in practice. We study a dynamic matching model where the designer minimizes a convex combination of mismatch count and vacancies, based on the Singaporean housing allocation process, Build-To-Order. With endogenous supply, the optimal mechanism overproduces underdemanded housing relative to the proportional benchmark, and competition over housing improves matching quality. Batching applications artificially generates competition and is optimal when the planner places a high weight on match quality.


Work in Progress


Teaching

Here I provide sample syllabi for second-year graduate classes in Empirical Industrial Organization and Urban Economics.

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