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.

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.

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.

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 vehicle ownership fluctuates by as much as S$10,000 per year per vehicle, possibly mispricing the negative externalities of ownership and use. To explain these large fluctuations in permit prices, we formulate a dynamic equilibrium model of vehicle ownership and replacement by individuals and firms. This model will be taken to administrative microdata to evaluate welfare under the current and alternative designs.