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.

Since low-income workers are overwhelmingly employed in non-tradable sectors, in response to transit expansion, changes in consumption travel patterns induce a spatial re-organization of low-income jobs in the city, with important implications for inequality. We develop an urban spatial model with heterogeneous worker groups, incorporating travel to consume non-tradable goods and services. The model is estimated using detailed farecard and administrative data from Singapore. After the Downtown Line was built, we find large welfare gains for high-income workers, but near zero for low-income workers. All workers benefit from improved access to consumption opportunities, but jobs in the low-income non-tradable sector move to less attractive workplaces. If we abstract away from consumption travel, we underestimate the line’s effect on inequality by a factor of five. Furthermore, the resulting model fails to capture the spatial re-organization of low-income jobs in the city.

How should the supply of public housing be optimally curated? Queuing mechanisms in the literature treat the supply of goods as exogenous. However, in practice, designers can often control the inflow of goods as well. We study a dynamic matching model based on the Singaporean housing allocation process. We show that endogenous supply radically changes the nature of the optimal mechanism. In this mechanism, a key feature is that under-demanded housing is overproduced relative to the static benchmark. Though competition leads to a decrease in efficiency when supply is exogenous, competition instead improves matching when supply is endogenous. Competition can be artificially generated through increasing the thickness of the market by batching applications. We show that doing so is a key feature of the optimal mechanism when the planner places a high weight on match quality.