Bertrand competition with capacity constraints: mergers among parking lots

Abstract To analyze the e ects of mergers among ÿrms facing capacity constraints, we develop a numerical model of price-setting behavior among multi-product ÿrms di erentiated by location and capacity. We perform a number of computational experiments designed to inform merger policy, with speciÿc reference to the Central Parking-Allright merger of 1999. The experiments show that capacity constraints on merging ÿrms attenuate merger e ects by much more than capacity constraints on non-merging ÿrms amplify them. The experiments also highlight the dependence of merger welfare e ects on parking demand. In preparation for further industry consolidation, we propose estimators of parking demand to more precisely estimate the costs and beneÿts of future mergers

Bertrand competition with capacity constraints: mergers among parking lots

Abstract To analyze the e ects of mergers among ÿrms facing capacity constraints, we develop a numerical model of price-setting behavior among multi-product ÿrms di erentiated by location and capacity. We perform a number of computational experiments designed to inform merger policy, with speciÿc reference to the Central Parking-Allright merger of 1999. The experiments show that capacity constraints on merging ÿrms attenuate merger e ects by much more than capacity constraints on non-merging ÿrms amplify them. The experiments also highlight the dependence of merger welfare e ects on parking demand. In preparation for further industry consolidation, we propose estimators of parking demand to more precisely estimate the costs and beneÿts of future mergers

Adversarial decision-making: Choosing between models constructed by interested parties

In this paper, we characterize adversarial decision-making as a choice between competing interpretations of evidence ("models") constructed by interested parties. We show that if a court cannot perfectly determine which party's model is more likely to have generated the evidence, then adversaries face a tradeoff: a model further away from the best (most likely) interpretation has a lower probability of winning, but also a higher payoff following a win. We characterize equilibrium when both adversaries construct optimal models, and use the characterization to compare adversarial decision-making to an inquisitorial benchmark. We find that adversarial decisions are biased, and the bias favors the party with the less-likely, and more extreme, interpretation of the evidence. Court bias disappears when the court is better able to distinguish between the likelihoods of the competing models, or as the amount of evidence grows

Adversarial decision-making: Choosing between models constructed by interested parties

In this paper, we characterize adversarial decision-making as a choice between competing interpretations of evidence ("models") constructed by interested parties. We show that if a court cannot perfectly determine which party's model is more likely to have generated the evidence, then adversaries face a tradeoff: a model further away from the best (most likely) interpretation has a lower probability of winning, but also a higher payoff following a win. We characterize equilibrium when both adversaries construct optimal models, and use the characterization to compare adversarial decision-making to an inquisitorial benchmark. We find that adversarial decisions are biased, and the bias favors the party with the less-likely, and more extreme, interpretation of the evidence. Court bias disappears when the court is better able to distinguish between the likelihoods of the competing models, or as the amount of evidence grows

Estimating Promotional Effects with Retailer-Level Scanner Data*

Abstract Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires only standard scanner data of the type produced by the major vendors. Using data for super-premium ice cream, we apply the proposed methodology to the random coefficients logit demand framework