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.

We evaluate the problem of firms that operate platforms matching buyers and sellers, while also selling goods on these same platforms. By being able to guide consumer search through algorithmic recommendations, these firms can influence market outcomes, a finding that has worried regulators. To analyze this phenomenon, we combine rich novel data about sales and recommendations on Amazon Marketplace with a structural model of intermediation power. In contrast to prior literature, we explicitly model seller entry. This feature enables us to assess the most plausible theory of harm from self-preferencing, i.e. that it is a barrier to entry. We find that recommendations are highly price elastic but favor Amazon. A substantial fraction of customers only consider recommended offers, and recommendations hence noticeably raise the price elasticity of demand. By preferring Amazon’s offer, the recommendation algorithm raises consumer welfare by approximately US$4.5 billion (since consumers also prefer these offers). However, consumers are made worse off if, as a consequence, the company raises prices by more than 7.8%. Furthermore, we find no evidence of consumer harm from self-preferencing through the entry channel. Nevertheless, entry matters. The algorithm raises consumer welfare in the short and medium run by increasing the purchase rate and intensifying price competition. However, these gains are mostly offset by reduced entry in the long run.

How does transit expansion impact welfare and inequality? Exploiting data covering the universe of transit farecard trips from Singapore, we propose a quantitative spatial model featuring heterogeneous worker groups and travel to consume non-tradable goods and services. First, we establish that low- and high-income workers live in and travel to different places. Second, we find that low-income workers are more sensitive to changes in travel time than their high-income counterparts. Third, since low-income workers are overwhelmingly employed in non-tradable sectors, we find that changes in consumption travel induced a spatial re-organization of low-income jobs in the city. We show that the Downtown Line resulted in large welfare gains for high-income workers, but near zero for low-income workers. All workers benefited from improved access to consumption opportunities, but low-income jobs in the non-tradable sector moved to less attractive workplaces. Abstracting from consumption travel underestimates the inequality effects five-fold.

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.