Organizational Structure and Pricing: Evidence from a Large U.S. Airline
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 dead-weight loss in the markets studied.
The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. We thank the anonymous airline for giving us access to the data used in this study. We also thank the seminar participants at Yale University, University of Virginia, Federal Reserve Bank of Minneapolis, University of Chicago, University of California-Berkeley, the NBER 2021 Summer Institute, the Virtual Quantitative Marketing Seminar, and the NBER Organizations Meeting for comments. We thank Matt Backus and Bob Gibbons for useful comments. We gratefully acknowledge financial support from the NET Institute, www.NETinst.org, the Becker Friedman Institute for Research in Economics, and the Tobin Center for Economic Policy.