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 Michaelmas (Fall) 2024, I will visit the Saïd Business School at the University of Oxford. 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.
My wife, Nicole Fry, paints landscapes.
- Apr 25 - May 2: Washington University in St. Louis
- Nov 26 - Dec 18: Saïd Business School, University of Oxford
- (TBC) LSE, UCL, Imperial, QMUL
- Feb 29: Land Transport Authority of Singapore
- Mar 15: NUS Applied Micro SIG
- Apr 3: USC Empirical IO (guest lecture, virtual)
- Apr 17: NUS Real Estate Brownbag
- Apr 22: University of Tokyo
- May 3-5: IIOC, Boston, MA
(Click on paper descriptions to see abstracts)
Urban transit infrastructure and inequality
We propose a quantitative spatial model featuring heterogeneous worker groups and their travel to consume non-tradable goods and services. We consider the opening of the Downtown Line (DTL) in Singapore, which connected regions where high-income households have residential amenities to where non-traded sectors are productive. Leveraging transit farecard data, we show that high-income workers saw large welfare gains but low-income workers gained little. Everyone enjoyed improved access to consumption opportunities, but low-income jobs in non-tradables moved to less attractive workplaces. Abstracting from consumption travel understates the disparate impact across worker groups three-fold.
(Click on paper descriptions to see abstracts)
Public housing at scale
- Joint work with Andrew Ferdowsian, Yiying
Tan and Luther
- Subsumes an earlier working paper “The dynamic
allocation of public housing: Policy and spillovers”.
- Revision (with microdata) in progress.
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.
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 often control the inflow of goods in practice. We study a dynamic matching model where the designer minimizes a convex combination of mismatch and vacancies, motivated by the Singaporean housing allocation process, Build-To-Order. The optimal mechanism overproduces underdemanded housing relative to the proportional benchmark, and competition over housing improves match quality. Batching applications artificially generates competition and is optimal when the planner places a high weight on match quality. Following our dissemination of an early version of this paper, the Singaporean government increased batching.
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.)
Distributional effects of vehicle ownership restrictions
- Joint work with Tiffany Tsai.
- Preliminary draft available upon request.
We study vehicle population controls, their dynamic effects, alternative policy designs, and implications for efficiency and equity. We leverage data on the universe of vehicles in Singapore, the number of which is limited by the Vehicle Quota Scheme (VQS). Under the VQS, car registrations must be accompanied by permits allocated in fortnightly auctions; after ten years, car permits expire and may be renewed at the prevailing auction price. First, we observe that the effective tax on car ownership fluctuates by as much as S$10,000 per year per car, possibly mispricing the negative externalities of car ownership and use. To explain these large fluctuations in permit prices, we present a theoretical example explaining herding in car replacement. When permit prices fall unexpectedly, car owners renew their permits early, further depressing prices. Turning to the data, we formulate a dynamic equilibrium model of car ownership and replacement, which we calibrate to match trends in permit prices and macroeconomic measures. This calibration informs our policy recommendations in advance of analysis with government microdata.
Work in Progress
- Seller multi-homing on platform marketplaces (with Liyu Dou and Ming Li)
- Impacts of rail connectivity on workers, firms, neighbourhoods, social mobility, and tax revenue (with Siddharth George)
- City design (with Brandon Joel Tan)
Here I provide sample syllabi for second-year graduate classes in Empirical Industrial Organization and Urban Economics.