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.

This paper investigates concerns that vertically integrated platforms like Amazon steer demand towards their own offers via algorithmic prominence, potentially harming consumers. On Amazon, for each product, the Buybox prominence algorithm selects one seller to feature, influencing which offers consumers consider. Using novel Amazon sales and Buybox (prominence) data, we estimate a structural model capturing the effects of such algorithmic prominence on consumer choices, seller pricing, and entry. We find that the platform can indeed steer demand as 95% of consumers consider only the Buybox offer. The Buybox is highly price-elastic (−21), but skews towards Amazon’s own offers, which are featured as frequently as observably similar offers priced 5% cheaper. Still, as consumers prefer these offers, this skew does not amount to self-preferencing in the sense of harming consumers: consumer surplus is roughly maximized at the estimated Amazon Buybox advantage, which balances higher prices against showing consumers their preferred offers.

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

Who gains and who loses when vehicle population controls are implemented? We study the distributional effects and alternative designs of the Vehicle Quota Scheme (VQS) in Singapore. Under the VQS, vehicle registrations must be accompanied by Certificates of Entitlement (COEs) allocated through fortnightly auctions; after ten years, COEs expire and may be renewed at the prevailing auction price. We first observe that COE prices vary by as much as S$10,000 per year per vehicle within the same vehicle class. While the VQS is part of a broader policy toolkit to manage congestion, we argue that its primary function today is revenue generation, with the potential for redistribution. To assess the implications of large price fluctuations, we formulate a dynamic equilibrium model of vehicle ownership and replacement and estimate it using administrative microdata. Higher COE prices between 2009 to 2014 fell most heavily on residents in the middle band of Singapore’s urban geography, relative to those in the outlying heartlands. Smoothing certificate supply over time could raise welfare by up to 0.85% in net present value by dampening price volatility. However, we find no evidence that shorter certificate durations improve welfare: more frequent renewals imply that consumers incur higher transaction costs than under the existing policy.

In many dynamic allocation markets, forward-looking agents reject offers to wait for better matches, creating inefficiency. We study this behavior in Singapore’s Build-to-Order (BTO) housing program, where apartments are allocated to married and engaged residents via a quarterly lottery and about 40% of offers are rejected. Using administrative microdata, we estimate a structural model of applicant behavior and simulate two policy interventions: deprioritizing applicants who reject acceptable offers and batching allocations into less frequent rounds. While banning rejections shortens queues, batching allocations, i.e., moving from quarterly to annual lotteries, yields higher welfare by improving match quality. Modest design changes can therefore meaningfully reduce waste and enhance welfare, though the most intuitive fix may not achieve the planner’s objectives.