- Limits to Buyer Power: Input Prices and Pass-Through in Horizontal Merger Policy (with Todd Lensman), Apr 2022 [Paper]
Abstract We re-examine the “buyer power” defense to horizontal mergers using models of imperfect competition in which input prices are set before goods prices. We derive a measure of unilateral incentives to adjust input prices after a downstream merger, Input Pricing Pressure, and we use it to show that mergers often incentivize higher input prices. Consumer surplus-maximizing antitrust policy is often too lax when input prices are assumed fixed, and it should be biased against buyer power claims. In an empirical application to local retail beer markets, endogenizing input prices substantially raises the consumer harm from mergers of retailers.
- Market Power Spillovers Across Airline Routes (with Roi Orzach), Jan 2023 [Paper]
Abstract Airlines often route connecting passengers through hubs. Despite the prevalence of connecting service, research analyzing the welfare effects of changes in competition focuses on nonstop routes. We show that when firms face capacity constraints or adjustment costs, a price decrease on a direct route may incentivize firms to decrease prices on indirect routes using this route as a leg. We call this relationship pass-through and document positive pass-through after entry by low-cost carriers and mergers. We show that ignoring these effects results in underestimating the consumer surplus effects of changes in competition between 9 and 115% across different competition shocks.
- Myopia in Dynamic Spatial Games (with Shane Auerbach), May 2019 [Paper - WSC Version - Slides]
Abstract We design an experiment to evaluate behavior in a dynamic spatial game representing the incentives faced by drivers for a ride-sharing service while waiting to be matched with a rider. The design is unique in that it allows us to observe not only participants’ choices, but also the considerations that went into those choices. The results of the experiment show that a large majority of player choices are consistent with myopic best responding. A myopic best response maximizes a player’s flow payoff at the time of the decision but is not necessarily optimal as it ignores strategic considerations regarding the future choices of opponents. Given the observed prevalence of this behavior and the challenges of equilibrium analysis, which we detail, we argue in favor of computational models of spatial competition built upon myopic agents. Myopic behavior in our model results in quite efficient outcomes, suggesting that ride-sharing companies may benefit from sharing with drivers the locations of other nearby drivers to allow them to compete spatially.
- A Neighborhood Search Heuristic for the p-Median Problem with Continuous Demand (with Shane Auerbach), May 2019 [Paper - Slides]
Abstract Computing optimal spatial allocations is important for two reasons. First, one may wish to implement them. Second, without a sense of an optimal spatial allocation, one cannot evaluate the efficiency of an observed spatial allocation. Suppose you have p facilities and wish to place them in a city to minimize the average distance between a consumer and her nearest facility. We develop a neighborhood search heuristic for this p-median problem with continuous demand. We discuss challenges to implementing the heuristic, propose solutions, and describe how it can be embedded in hybrid heuristics. We then apply the heuristic to computing optimal spatial allocations of facilities in Chicago, Atlanta, and Los Angeles. In comparing these optimal allocations to the actual ones, we find that allocations of supermarkets do relatively poorly in minimizing transportation costs for consumers.