Publication Date: November 2021
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.
Keywords: Pricing Frictions, Organizational Inertia, Dynamic Pricing, Revenue Management, Behavioral IO
JEL Classification Codes: C11, C53, D22, D42, L10, L93CFDP 2312R