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Cowles Foundation Discussion Papers

2021

Pricing Frictions

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Organizational Structure And Pricing: Evidence From A Large U.S. Airline, Ali Hortaçsu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg, Kevin R. Williams Nov 2021

Organizational Structure And Pricing: Evidence From A Large U.S. Airline, Ali Hortaçsu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg, Kevin R. Williams

Cowles Foundation Discussion Papers

We study how organizational boundaries affect pricing decisions using comprehensive data from a large U.S. airline. We document that the firm’s advanced pricing algorithm, utilizing inputs from different organizational teams, is subject to multiple biases. To quantify the impacts of these biases, we estimate a structural demand model using sales and search data. We recover the demand curves the firm believes it faces using forecasting data. In counterfactuals, we show that correcting biases introduced by organizational teams individually have little impact on market outcomes, but coordinating organizational outcomes leads to higher prices/revenues and increased deadweight loss in the markets studied.


Organizational Structure And Pricing: Evidence From A Large U.S. Airline, Ali Hortaçsu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg, Kevin R. Williams Nov 2021

Organizational Structure And Pricing: Evidence From A Large U.S. Airline, Ali Hortaçsu, Olivia R. Natan, Hayden Parsley, Timothy Schwieg, Kevin R. Williams

Cowles Foundation Discussion Papers

Although typically modeled as a centralized firm decision, pricing often involves multiple organizational teams that have decision rights over specific pricing inputs. We study team input decisions using comprehensive data from a large U.S. airline. We document that pricing at a sophisticated firm is subject to miscoordination across teams, uses persistently biased forecasts, and does not account for cross-price elasticities. With structural demand estimates derived from sales and search data, we find that addressing one team’s biases in isolation has little impact on market outcomes. We show that teams do not optimally account for biases introduced by other teams. We …