Learning to Coordinate: A Study in Retail Gasoline

By David P. Byrne, Nicolas D B. Roos. The American Economic Review, 2019

171 citations recorded by the source corpus.

This paper studies equilibrium selection in the retail gasoline industry. We exploit a unique dataset that contains the universe of station-level prices for an urban market for 15 years, and that encompasses a coordinated equilibrium transition mid-sample. We uncover a gradual, three-year equilibrium transition, whereby dominant firms use price leadership and price experiments to create focal points that coordinate market prices, soften price competition, and enhance retail margins. Our results inform the theory of collusion, with particular relevance to the initiation of collusion and equilibrium selection. We also highlight new insights into merger policy and collusion detection strategies. (JEL G34, L12, L13, L71, L81, Q35)

Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/ecfb7edaaff9df05c8c336ebd74907e21df9c6b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Alexander Mackay
2. Daniel Ershov
3. Robert Clark
4. S. Assad
5. Lei Xu
6. Zach Y. Brown

In Management Science and Operations Research

1. Alexander Mackay
2. Daniel Ershov
3. Robert Clark
4. S. Assad
5. Lei Xu
6. Zach Y. Brown

In Marketing

1. Alexander Mackay
2. Daniel Ershov
3. Robert Clark
4. S. Assad
5. Lei Xu
6. Zach Y. Brown

Citations in the corpus

• Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market (2023, methodology): “In the second step, “learning,” the gas station agent uses an Artificial Neural Network (ANN) to map inputs into outcomes.(12) The outcomes are not explicitly outlined in Derakhshan et al (2016) but they likely correspond to sales, revenues and/or profits.”
• Competition in Pricing Algorithms (2020, background): “Calvano et al. (2019) show that this concern applies to standard reinforcement-learning algorithms by demonstrating that they converge to supracompetive prices in simulations. Two recent papers show that better demand forecasting can have ambiguous effects on consumer welfare. Miklós-Thal and Tucker”