In many markets, advance interim contracts include an explicit right to renege, granting one party the option to switch to more efficient matches that emerge later in the search process. This paper studies the formation and welfare implications of such interim contracts, leveraging novel data from a brokerage firm in the trucking industry. The broker allocates advance contracts on shipments to carriers through a dynamic auction mechanism and penalizes cancellations through a reputational mechanism. I develop a theoretical model linking the carrier’s bidding problem to the firm’s cancellation penalties through a dynamic job-search problem and structurally estimate the model from rich data on bids and cancellations. In counterfactual simulations, I show that the firm is incentivized to lower cancellation penalties as the option value of the right to renege is priced into carrier bids. The results rationalize the large degree of contractual flexibility observed in the trucking industry as an efficient market outcome rather than one constrained by limited contractual enforcement.

I study oligopolistic dynamic price competition between micromobility platforms in Washington, D.C. The model presents a computational challenge due to both the large policy space—optimizing prices across hundreds of origin-destination pairs for eight competing firms—and the large state space of vehicle inventories across different locations. I overcome these computational challenges by performing gradient descent in a simulated dynamic model of the market, an approach proven to be successful in high-dimensional settings by the deep reinforcement learning literature. Using this framework, I compare the current regulation-constrained regime of spatially uniform price competition with counterfactual scenarios with competition on fully flexible prices for each origin-destination pair and time of day, focusing on the distributional impacts of flexible pricing. Matching the heterogenous welfare impacts to trip survey data, I find that welfare gains and losses are evenly distributed across travelers of different incomes, allaying policymakers’ concerns of equitable access to transportation under destination pricing.

We study equilibrium market structure and pricing across the nationwide freight trucking network, a trillion-dollar market responsible for moving 72% of the nation's goods. Using detailed auction data from the U.S. freight spot market, we document a three-fold per-mile price dispersion across 171 U.S. cities, evidence of significant geographic labor supply preferences, and imbalanced trade flows between regions. To understand these patterns, we pose a micro-founded model of carrier bidding behavior across local markets. Local market outcomes are linked to each other in a spatial equilibrium, as the movement of trucks in the network influences the value of bidding on different shipment destinations. The auction-based setting allows us to obtain rich estimates of carrier cost heterogeneity, search frictions and home-region preferences. Despite the large and competitive national pool of carriers and drivers, markets with a thinner flow of trucks in equilibrium give rise to localized market power. We use our estimates to quantify how these factors contribute to the observed spatial price dispersion and capacity patterns over the network.

Superstar effects have been linked to rising income inequality since Rosen (1981). Theoretical models assume that superstar effects are driven by natural market forces allowing high-quality individuals to command disproportionately large market shares—especially in industries with near-zero marginal costs of production, such as entertainment. However, choice inertia induced by search frictions can further amplify the success of superstars through an incumbency advantage. We provide novel evidence quantifying the impact of choice inertia on superstar popularity within the live-streaming market. We analyze panel data on viewership for a game category before and after a significant demand shock, which spurred entry of new streamers. Our findings reveal that while incumbent streamers are more popular overall, new viewers are twice as likely (32.81\%) as incumbents (14.63\%) to watch an entrant streamer, highlighting a substantial role for choice inertia. These insights can guide the design of content recommendation algorithms to support new creators and mitigate superstar effects caused by choice inertia.

We utilize data from 5,010 soccer games in the top two Swiss divisions between the 2005/06 and 2018/19 seasons. In these games, a referee can share the same linguistic area with one of the teams. Using referee-per-season fixed effects, we find that referees issue significantly more penalties, in the form of yellow cards, to teams that are not from the referee's linguistic area. We also find some evidence, in the highest level league only, that referees issue more red cards to teams that are not from their linguistic area and that away teams achieve fewer points when home teams share the same linguistic area with the referee. Our analyses suggest that referees' bias is likely to be subconscious and reflexive rather than being a deliberate act of discrimination.