Tuyetanh L. Tran

City governments face a trade-off in managing curb space: providing parking to facilitate access to consumption amenities and generate revenue, versus allocating it to alternative land uses. In this paper, we quantify the welfare implications of curbside parking and evaluate alternative policies for managing curb space through parking instruments. We develop a structural model of drivers' joint destination and parking decisions: drivers choose which destination to visit under imperfect information about parking availability, then decide where to park near the chosen destination. We estimate the model using high-frequency data on metered parking transactions and GPS data on visits to points of interest in San Francisco, one of the few cities that have implemented demand-based pricing for curbside parking. We find that, while drivers value curbside parking, the present discounted value of parking revenue and driver surplus generally falls short of local assessed land values, which proxy for the economic value of land uses. Compared to a revenue-maximizing uniform pricing scheme, San Francisco's demand-based pricing generates about 30% more revenue while reducing cruising trips by nearly 70%. Our counterfactuals show that reducing parking supply by roughly 6% and lowering the status quo demand-based prices by $1.25 citywide preserves parking welfare, with only a modest revenue loss, while freeing curb space for other uses.

We study welfare gains from innovations in the global markets for liquid crystal display (LCD) panels. We show direct evidence on both product and process innovations by using detailed data on sales, costs, and investments. We then estimate a structural model of demand and supply to quantify their contributions. Results suggest social return on technological investment was positive, but private gains for most firms were negligible due to competition. We further investigate how competition affects firms’ incentives to innovate in a series of counterfactual simulations with hypothetical mergers.

In this project, we study how competition and spillovers (e.g., scale economies, trip chaining) among nearby urban amenities shape their entry and location choices under persistent shocks, such as work-from-home (WFH) adoption and rezoning. We combine San Francisco's business registry, which records establishment entry, exit, and relocation, with Veraset GPS foot-traffic data to examine the supply and demand for urban amenities. We document that establishments within small spatial units (e.g., Census block groups) can attract noticeably different amounts of foot traffic, controlling for amenity category, parcel size, and brand name. We interpret this variation as evidence of local quality heterogeneity. This within-location heterogeneity gives rise to spillovers across amenity categories and between establishments of different quality levels, which would otherwise be masked in a homogeneous-firm setting. Motivated by this evidence, we develop a spatial equilibrium model in which heterogeneous consumers choose amenities and heterogeneous establishments choose locations, accounting for competition and local spillovers. This framework allows us to evaluate counterfactual WFH and rezoning policies. WFH reduces worker demand in the city, while rezoning, such as converting offices to housing or retail space, shifts demand from workers to residents and incentivizes amenity entry through increased retail space. These shocks are expected to reshape the city's retail landscape significantly.